This document describes preliminary work on controlling a fully inflatable, fabric-based humanoid robot using pneumatic actuation. Model predictive control and linear quadratic regulation are shown to provide sufficient position control of a single-joint robot. An inflatable humanoid robot with one arm and five degrees of freedom is also controlled using these methods. An initial task of picking up and moving an object was successful eight out of ten times using the model predictive controller. The work demonstrates high-level control of a fully inflatable soft humanoid robot and suggests this type of robot could interact more safely with humans.
Robotics and control theory have a long history of interaction, with each field helping to advance the other. Early industrial robots had independent joint control without sensing, but consideration of tasks like assembly required new control methods. Developments in nonlinear, robust, and adaptive control enabled more sophisticated robot applications. Today, advances in computation, sensors, and networking have led to applications like mobile robots, surgical robots, and robot networks playing increasing roles in society. Key challenges going forward include control of underactuated, legged, and multi-agent robotic systems.
1) The document discusses the fundamentals of robotic manipulators, including their classification, parts, motions, and work envelopes.
2) The major types of robot configurations are Cartesian, cylindrical, spherical, SCARA, and articulated, which are defined by their joint types and resulting work spaces.
3) Robotic manipulators consist of links connected by joints and powered by electric, hydraulic, or pneumatic drives to position an end effector through programmed motions.
This document proposes an adaptive synchronized control method for coordinating multirobot assembly tasks. The key points are:
- It uses a synchronization scheme based on motion synchronization to maintain certain kinematic relationships between robots, without requiring complex hybrid position/force control.
- The controller incorporates cross-coupling technology into an adaptive control architecture. It guarantees asymptotic convergence to zero of both position tracking errors and synchronization errors simultaneously for coordinated robots.
- The method is applied first to coordinate two robots holding a common payload. It is then extended to coordinate multiple robots, where each robot synchronizes its motion with the other two robots to achieve overall synchronization. Experiments demonstrate the effectiveness of the approach.
Virtual Prototype with Rigid and Flexi-body Concept to Develpoment of Multifu...Journal For Research
In the actual world, any unremitting medium deforms under the application of force. Rigid body simulations do not confine such deformations and may lead to inexact results. As a virtual prototype replaces physical prototype, tests can be carried out to validate the design of the product. Virtual prototype of multifunctional robot is developed to simulate the system level working with live stress distribution, which is designed to lift a specific payload. Robot arm is converted into flexi-body as it is critical component, the maximum live Vonmises stress developed in the robot arm is less than yield stress of the steel material considered and found that the working of actual robot found satisfactory. In this work design and analysis will rely heavily on tool namely, CATIA V5R19 and NASTRAN/PATRAN.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Optimal Control of a Teleoperation System via LMI- based Robust PID Controllersidescitation
This document summarizes a research paper that proposes a new robust PID controller for a teleoperation system using an LMI (linear matrix inequality) approach. The controller aims to achieve transparency and robust stability despite uncertainties like time delays in communication channels and parameters of the slave manipulator and remote environment. The proposed method involves designing two local controllers - a slave controller at the remote site to track master commands, and a robust PID master controller at the local site to ensure transparency and stability. Simulation results will compare the proposed controller to a conventional multi-objective H2/H-infinity controller.
This document discusses metamorphic robots, which are collections of independently controlled modules that can dynamically self-reconfigure their shape without human intervention by connecting, disconnecting, and moving adjacent modules. Key concepts discussed include digital hormones for communication between modules, how modules propagate hormones to specify tasks and resolve conflicts, applications of metamorphic robots in planetary exploration and hazardous environments, and future improvements related to sensors, distributed control, motion planning, and connection mechanisms. Metamorphic robots could enhance capabilities for space activities and enable adaptability to new tasks.
Robotics and control theory have a long history of interaction, with each field helping to advance the other. Early industrial robots had independent joint control without sensing, but consideration of tasks like assembly required new control methods. Developments in nonlinear, robust, and adaptive control enabled more sophisticated robot applications. Today, advances in computation, sensors, and networking have led to applications like mobile robots, surgical robots, and robot networks playing increasing roles in society. Key challenges going forward include control of underactuated, legged, and multi-agent robotic systems.
1) The document discusses the fundamentals of robotic manipulators, including their classification, parts, motions, and work envelopes.
2) The major types of robot configurations are Cartesian, cylindrical, spherical, SCARA, and articulated, which are defined by their joint types and resulting work spaces.
3) Robotic manipulators consist of links connected by joints and powered by electric, hydraulic, or pneumatic drives to position an end effector through programmed motions.
This document proposes an adaptive synchronized control method for coordinating multirobot assembly tasks. The key points are:
- It uses a synchronization scheme based on motion synchronization to maintain certain kinematic relationships between robots, without requiring complex hybrid position/force control.
- The controller incorporates cross-coupling technology into an adaptive control architecture. It guarantees asymptotic convergence to zero of both position tracking errors and synchronization errors simultaneously for coordinated robots.
- The method is applied first to coordinate two robots holding a common payload. It is then extended to coordinate multiple robots, where each robot synchronizes its motion with the other two robots to achieve overall synchronization. Experiments demonstrate the effectiveness of the approach.
Virtual Prototype with Rigid and Flexi-body Concept to Develpoment of Multifu...Journal For Research
In the actual world, any unremitting medium deforms under the application of force. Rigid body simulations do not confine such deformations and may lead to inexact results. As a virtual prototype replaces physical prototype, tests can be carried out to validate the design of the product. Virtual prototype of multifunctional robot is developed to simulate the system level working with live stress distribution, which is designed to lift a specific payload. Robot arm is converted into flexi-body as it is critical component, the maximum live Vonmises stress developed in the robot arm is less than yield stress of the steel material considered and found that the working of actual robot found satisfactory. In this work design and analysis will rely heavily on tool namely, CATIA V5R19 and NASTRAN/PATRAN.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Optimal Control of a Teleoperation System via LMI- based Robust PID Controllersidescitation
This document summarizes a research paper that proposes a new robust PID controller for a teleoperation system using an LMI (linear matrix inequality) approach. The controller aims to achieve transparency and robust stability despite uncertainties like time delays in communication channels and parameters of the slave manipulator and remote environment. The proposed method involves designing two local controllers - a slave controller at the remote site to track master commands, and a robust PID master controller at the local site to ensure transparency and stability. Simulation results will compare the proposed controller to a conventional multi-objective H2/H-infinity controller.
This document discusses metamorphic robots, which are collections of independently controlled modules that can dynamically self-reconfigure their shape without human intervention by connecting, disconnecting, and moving adjacent modules. Key concepts discussed include digital hormones for communication between modules, how modules propagate hormones to specify tasks and resolve conflicts, applications of metamorphic robots in planetary exploration and hazardous environments, and future improvements related to sensors, distributed control, motion planning, and connection mechanisms. Metamorphic robots could enhance capabilities for space activities and enable adaptability to new tasks.
This ppt will give you information about space robotics, its applications and how much important role they are doing in day to day life viz; reducing human efforts,pick and place,marketing,etc.
This document discusses research using Bayesian optimization to determine effective locomotion techniques for tensegrity robots. The researchers developed code to control vibrating motors on a tensegrity robot and track its movement. Bayesian optimization was used to attempt to find optimal vibration frequencies that produced fast, straight movement of the robot. Several frequency sets were found that resulted in rapid straight movement, but further investigation is needed to confirm Bayesian optimization is the best approach for tensegrity locomotion learning.
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...IRJET Journal
1. The document discusses using model predictive control and reinforcement learning to teach a robotic leg to jump a certain height. Model predictive control is used to regulate the leg's dynamics like torque and angles, while reinforcement learning helps the leg adapt through trial and error.
2. Reinforcement learning algorithms like PPO and A2C are applied to give feedback based on successful or unsuccessful jumps. This helps the robotic leg learn over time to precisely jump the target height.
3. Legged robots have an advantage over wheeled robots in navigating uneven terrain. Quadruped robots like Boston University's Mini Cheetah can move quickly over varied surfaces using model predictive control of its leg actuators and sensors.
Improving Posture Accuracy of Non-Holonomic Mobile Robot System with Variable...TELKOMNIKA JOURNAL
This paper presents a method to decrease imprecision and inaccuracy that have the tendency to
influence the posture of non-holonomic mobile robot by using the adaptive tuning of universe of discourse.
As such, the primary objective of the study is to force the posture error of , , and towards
zero. Hence, for each step of tuning the fuzzy domain, about 20% of imprecision and inaccuracy had been
added automatically into the variable universe fuzzy, while the control input was bound via scaling gain.
Furthermore, the simulation results showed that the tuning of universe fuzzy parameters could increase
the performance of the system from the aspects of response time and error for steady state through better
control of inaccuracy. Besides, the domains of universe fuzzy input [-4,4] and output [0,6] exhibited good
performance in inching towards zero values as the steady state error was about 1% for x(t) position, 0.02%
for y(t) position, and 0.16% for θ(t) orientation, whereas the posture error in the given reference was about
0.0002% .
The document describes Mojtaba Hajimiri's MSc thesis which aimed to regulate interaction force between a robot and soft tissue during minimally invasive surgery using impedance control with a Hunt-Crossley nonlinear tissue model. The thesis contained theoretical, simulation, and experimental components, with the experimental setup using a pneumatic system, MATLAB real-time control, and various sensors to test the proposed force tracking impedance control approach.
A review article summarizes swarm robotics, including its key characteristics, behaviors inspired by nature, and applications. The article discusses simulating swarm behaviors and deploying them using robot hardware. It reviews the evolution of swarm robotics from early research to current applications and outlines future directions, with the goal of bridging theory and industrial practice.
Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Secon...CSCJournals
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots
This document describes the development of an autonomous mobile robot for wall following using a fuzzy incremental controller. Two ultrasonic sensors are used to sense the distance to the wall and provide input to the controller. The controller determines the speed of two DC motors to guide the robot along the wall. Experimental results showed the fuzzy controller successfully controlled the robot to follow the wall, performing better than a PID controller. The robot is intended for applications like cleaning air ducts or corridors by autonomously navigating while maintaining a set distance from the wall.
Wall follower autonomous robot development applying fuzzy incremental controllerrajabco
This paper presents the design of an autonomous robot as a basic development of an intelligent wheeled mobile robot for air duct or corridor cleaning. The robot navigation is based on wall following algorithm. The robot is controlled us- ing fuzzy incremental controller (FIC) and embedded in PIC18F4550 microcontroller. FIC guides the robot to move along a wall in a desired direction by maintaining a constant distance to the wall. Two ultrasonic sensors are installed in the left side of the robot to sense the wall distance. The signals from these sensors are fed to FIC that then used to de- termine the speed control of two DC motors. The robot movement is obtained through differentiating the speed of these two motors. The experimental results show that FIC is successfully controlling the robot to follow the wall as a guid- ance line and has good performance compare with PID controller.
Design and Development of Low Cost 3D Printed Ambidextrous Robotic Hand Drive...Mashood Mukhtar
This paper presents the mechanical design and development process of an ambidextrous robot hand driven by pneumatic muscles. The ambidextrous hand is capable of performing both right hand and left hand movements. In addition to ambidextrous movements, hand offers a range twice larger than common fingers. The mechanical design of an ambidextrous robot has been investigated in a way to reduce
maximum possible number of actuators. Actuated by only 18
pneumatic muscles, the ambidextrous hand has a total of 13
degrees of freedom which permit to imitate equally a hand of
each side. The ambidextrous hand is 3D printed after carefully
analyzing the material, tendon routing, kinematic performance
and overall design parameters. The main application areas of
this project are in rehabilitation and physiotherapy after strokes and management of phantom pain for amputees by controlling the robotic prosthesis remotely via internet and social media interface. The ambidextrous feature of the robotic hand allows completing the tele-rehabilitation for both left and right hands using one robotic device.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document defines and classifies robots. It explains that a robot is a programmable manipulator designed to perform tasks. Industrial robots are used for manufacturing and have declining costs. Robots have links, joints, and degrees of freedom that determine their motion. They are classified by drive type, kinematic structure, degrees of freedom, and workspace geometry. Common robot types include Cartesian, cylindrical, spherical, articulated, and SCARA robots.
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
This ppt will give you information about space robotics, its applications and how much important role they are doing in day to day life viz; reducing human efforts,pick and place,marketing,etc.
This document discusses research using Bayesian optimization to determine effective locomotion techniques for tensegrity robots. The researchers developed code to control vibrating motors on a tensegrity robot and track its movement. Bayesian optimization was used to attempt to find optimal vibration frequencies that produced fast, straight movement of the robot. Several frequency sets were found that resulted in rapid straight movement, but further investigation is needed to confirm Bayesian optimization is the best approach for tensegrity locomotion learning.
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...IRJET Journal
1. The document discusses using model predictive control and reinforcement learning to teach a robotic leg to jump a certain height. Model predictive control is used to regulate the leg's dynamics like torque and angles, while reinforcement learning helps the leg adapt through trial and error.
2. Reinforcement learning algorithms like PPO and A2C are applied to give feedback based on successful or unsuccessful jumps. This helps the robotic leg learn over time to precisely jump the target height.
3. Legged robots have an advantage over wheeled robots in navigating uneven terrain. Quadruped robots like Boston University's Mini Cheetah can move quickly over varied surfaces using model predictive control of its leg actuators and sensors.
Improving Posture Accuracy of Non-Holonomic Mobile Robot System with Variable...TELKOMNIKA JOURNAL
This paper presents a method to decrease imprecision and inaccuracy that have the tendency to
influence the posture of non-holonomic mobile robot by using the adaptive tuning of universe of discourse.
As such, the primary objective of the study is to force the posture error of , , and towards
zero. Hence, for each step of tuning the fuzzy domain, about 20% of imprecision and inaccuracy had been
added automatically into the variable universe fuzzy, while the control input was bound via scaling gain.
Furthermore, the simulation results showed that the tuning of universe fuzzy parameters could increase
the performance of the system from the aspects of response time and error for steady state through better
control of inaccuracy. Besides, the domains of universe fuzzy input [-4,4] and output [0,6] exhibited good
performance in inching towards zero values as the steady state error was about 1% for x(t) position, 0.02%
for y(t) position, and 0.16% for θ(t) orientation, whereas the posture error in the given reference was about
0.0002% .
The document describes Mojtaba Hajimiri's MSc thesis which aimed to regulate interaction force between a robot and soft tissue during minimally invasive surgery using impedance control with a Hunt-Crossley nonlinear tissue model. The thesis contained theoretical, simulation, and experimental components, with the experimental setup using a pneumatic system, MATLAB real-time control, and various sensors to test the proposed force tracking impedance control approach.
A review article summarizes swarm robotics, including its key characteristics, behaviors inspired by nature, and applications. The article discusses simulating swarm behaviors and deploying them using robot hardware. It reviews the evolution of swarm robotics from early research to current applications and outlines future directions, with the goal of bridging theory and industrial practice.
Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Secon...CSCJournals
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots
This document describes the development of an autonomous mobile robot for wall following using a fuzzy incremental controller. Two ultrasonic sensors are used to sense the distance to the wall and provide input to the controller. The controller determines the speed of two DC motors to guide the robot along the wall. Experimental results showed the fuzzy controller successfully controlled the robot to follow the wall, performing better than a PID controller. The robot is intended for applications like cleaning air ducts or corridors by autonomously navigating while maintaining a set distance from the wall.
Wall follower autonomous robot development applying fuzzy incremental controllerrajabco
This paper presents the design of an autonomous robot as a basic development of an intelligent wheeled mobile robot for air duct or corridor cleaning. The robot navigation is based on wall following algorithm. The robot is controlled us- ing fuzzy incremental controller (FIC) and embedded in PIC18F4550 microcontroller. FIC guides the robot to move along a wall in a desired direction by maintaining a constant distance to the wall. Two ultrasonic sensors are installed in the left side of the robot to sense the wall distance. The signals from these sensors are fed to FIC that then used to de- termine the speed control of two DC motors. The robot movement is obtained through differentiating the speed of these two motors. The experimental results show that FIC is successfully controlling the robot to follow the wall as a guid- ance line and has good performance compare with PID controller.
Design and Development of Low Cost 3D Printed Ambidextrous Robotic Hand Drive...Mashood Mukhtar
This paper presents the mechanical design and development process of an ambidextrous robot hand driven by pneumatic muscles. The ambidextrous hand is capable of performing both right hand and left hand movements. In addition to ambidextrous movements, hand offers a range twice larger than common fingers. The mechanical design of an ambidextrous robot has been investigated in a way to reduce
maximum possible number of actuators. Actuated by only 18
pneumatic muscles, the ambidextrous hand has a total of 13
degrees of freedom which permit to imitate equally a hand of
each side. The ambidextrous hand is 3D printed after carefully
analyzing the material, tendon routing, kinematic performance
and overall design parameters. The main application areas of
this project are in rehabilitation and physiotherapy after strokes and management of phantom pain for amputees by controlling the robotic prosthesis remotely via internet and social media interface. The ambidextrous feature of the robotic hand allows completing the tele-rehabilitation for both left and right hands using one robotic device.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document defines and classifies robots. It explains that a robot is a programmable manipulator designed to perform tasks. Industrial robots are used for manufacturing and have declining costs. Robots have links, joints, and degrees of freedom that determine their motion. They are classified by drive type, kinematic structure, degrees of freedom, and workspace geometry. Common robot types include Cartesian, cylindrical, spherical, articulated, and SCARA robots.
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONING
Humanoids 2015 Paper
1. Control of a Pneumatically Actuated, Fully Inflatable, Fabric-based,
Humanoid Robot
Charles M. Best Joshua P. Wilson Marc D. Killpack
Abstract— Although humanoid robots take the form of hu-
mans, these robots often approach manipulating the world in a
very different way than humans. For example, many humanoid
robots require precise position control and geometric models to
interact successfully with the world. Humanoid robots also often
avoid making contact with the world unless the contact can
be well modeled. In this work, we present preliminary results
on soft robot platforms that can change the way humanoid
robots interact with humans and human environments. We
present preliminary control methods and testing on fully in-
flatable, pneumatically actuated, soft robots. We first show that
model predictive control (MPC) and linear quadratic regulation
(LQR) are sufficient for position control of a single joint with
one degree of freedom. We also demonstrate MPC and LQR
as methods of control for an inflatable humanoid robot on one
arm using five degrees of freedom. Our initial development for
multi-joint control is based on the methods developed for the
single degree of freedom platform. Using the MPC controller
with joint space commands, a task of picking up a board from
a chair and placing it in a box was successful eight out of ten
times. Our models and control methods will allow for a new
type of humanoid robots that are well suited to interacting more
safely and naturally in human environments.
I. INTRODUCTION
There has been the promise for many years of robots
revolutionizing the way that humans accomplish work and
interact with the world. Humanoid robots especially have
been the recent subject of large-scale endeavors such as the
DARPA robotics challenge. Although the results from past
research have been impressive, in this paper, we present a
fundamentally different robot platform and approach to con-
trolling this platform than past humanoid robotics research.
In particular, much of the current humanoid robot research
uses position controlled robot arms for manipulation which
requires accurate geometric and sometimes accurate models
of contact dynamics to be successful. Using a soft, humanoid
robot platform that is inherently more robust to contact with
the world, humans, and even other robots is an approach that
may be fruitful for advances in manipulation in unstructured
environments.
The research in this paper demonstrates the first published
work that we are aware of on high level control for a
pneumatically actuated, fully inflatable, soft humanoid robot
(shown in Figure 1). Pneumatic actuation means that joint
torques are produced through the use of compressed air. Fully
inflatable means that the system structure comes from pres-
surized chambers within a non-rigid medium such as fabrics
or polymers. A robot of this type is naturally lightweight
and compliant which can be desirable in many situations but
does result in greater complexity and difficulty in sensing,
actuation, and control [1].
Fig. 1: Inflatable Humanoid Robot named King Louie
Our specific contributions include the following:
• Development of a state space impedance model that
predicts future states of an antagonistic, pneumatically
actuated joint.
• Development of a joint space Model Predictive Con-
troller for soft humanoid robots which is shown to
have comparable performance to the more convention-
ally used Linear Quadratic Controller while allowing
realistic system constraints to be incorporated.
• Demonstration of control for a real inflatable, fabric
based robots with pneumatic actuation that can be
controlled to repeatedly accomplish useful tasks.
This paper is organized as follows. Section II discusses
past work relevant to this research. A description of the
systems for which we have implemented control is in Section
III. In Section IV we then discuss the model and formulation
for MPC and LQR controllers for a single degree of freedom
joint along with the system response to the controllers. The
use of a MPC and LQR controller on a multiple degree of
freedom arm is then discussed in Section V.
II. RELATED WORKS
The compliant and lightweight characteristics of this sys-
tem are desirable for many applications. This is in part
because there has been significant interest in making robots
2. more effective at interacting with humans or human en-
vironments. Robots currently have limited uses in homes,
hospitals, schools, or other areas where safe interaction with
people or the environment may be necessary. One reason why
robots are not common in these places is because robots can
be dangerous to people or property when there is incidental
contact.
Lightweight robots such as our platform have less inertia
and are less likely to cause harm because of lower contact
forces and lower overall momentum when moving at varying
speeds. Our research is motivated by a desire to take ad-
vantage of the positive characteristics of soft and inflatable
robotic systems while maintaining a level of control that will
allow them to be useful. Applications for a robot of this type
include search and rescue, health care, living assistance, and
space exploration.
Research has shown that contact forces from inflatable
links can be controlled in [2] and [3] with cable driven
actuators. While cable driven actuators are an effective means
of actuation for inflatable structures, our work will focus
on pneumatic actuation as this is more consistent with the
design intent for inflatable structures. In [4], [5], and [6] it
was found that planning is possible for fluid driven elastomer
actuators using dynamic and constant curvature kinematic
models which is a similar actuation method to ours. The
fabric designs for our actuators were based from the designs
for rotary, fabric-based, pneumatic actuated joints which
were proposed in [7]. For rigid, traditional servo-pnuematic
actuators, work in [8] characterized different models which
made different constant temperature assumptions and in [9]
these assumptions were used to control force and stiffness for
a linear pneumatic actuator. Past research involving inflatable
robots in general looks at the performance of an actuator
or a series of actuators. We show that an entire system
can be inflatable and controls can be developed for the
system to effectively complete tasks normally done by a
robot with rigid structure. The lack of literature on the
control of inflatable structures where there is a wide range
of applications suggests a viable new area for research.
Similar to the actuators on our robot, pneumatic artificial
muscles called McKibben artificial muscles have been de-
veloped that use compressed air, and are used in [10] and
[11] as orthotic devices. McKibben artificial muscles have
also been used in robots, and control methods have been
developed for these robots in [12] and [13]. Our robot differs
from these in that McKibben muscles exert a tension force
when pressurized whereas the actuators in our robot exhibit a
torque on the joint from compressive forces. Probably more
important is that prior researchers use actuators in parallel
with a rigid-link system which is significantly different than
using a fully inflatable system.
In terms of methods used to control our robot, Model
predictive control (MPC) is an optimal control method that
has long been used in the chemical processing industry
[14]. Recent advances in computing power and dynamic
optimization techniques such as those presented in [15], [16]
have made MPC a viable control method in applications
that require a high control rate. MPC has been demonstrated
and shown promise in many areas outside of the chemical
processing industry including control of Unmanned Aerial &
Surface Vehicles [17], [18], [19] and even more recently in
robotics [20], [21], [22], [23], [24].
Because MPC is not commonly used in robotics, in par-
ticular humanoid robots, the more conventional and widely
accepted linear quadratic control is applied to the system
to show that MPC is a viable control method. MPC and
LQR control methods are very similar and overlap in some
cases as seen in [25] where constraints were added to the
LQR controller with a finite horizon making it MPC. It was
seen in [26] that LQR methods are viable options for full
body humanoid robots. LQR methods have also been used
for humanoid tasks such as balance [27] and walking [28].
III. ROBOT PLATFORM DESCRIPTION
The platform used for this research is a fourteen degree of
freedom humanoid robot called King Louie (Figure 1) and
a single degree of freedom joint called a grub (Figure 2).
Both were developed and built by Pneubotics, an affiliate of
Otherlab. Besides internal electronics such as IMU and pres-
sure sensors, these platforms are entirely made of ballistic
nylon fabric with internal bladders to prevent air leakage. The
structure of these robots comes from an inflatable bladder
which is pressurized to between 1-2 PSI gage. At each
joint, there are antagonistic actuators which can be filled to
pressures between 0-25 PSI gage. For this research we use
pressures between 0-17 PSI gage because of pinching effects
in the main chamber at higher pressures. These actuators are
similar to the designs mentioned in [7].
Fig. 2: This is a single degree of freedom platform that we
call a “grub.”
Initial work for model development and control of our
inflatable robotic systems was done with the grub. After
we performed initial analysis and testing on the 1-DoF
system (grub) we applied the same methods to the more
complex 5-DoF arm on our humanoid system (King Louie).
King Louie’s arm configuration, orientation, and motion can
be approximated with Denavit-Hartenberg parameters using
an assumption of rigid links and compliant joints. These
parameters are shown in Table I with values for a and d
in meters, and θ and α in radians.
3. TABLE I: DH Parameters for King Louie.
1 2 3 4 End
θ q1 + π
2
q2 q3 q4 q5 + π
d -0.05 0 0 0 0
a 0.18 0.32 0.28 0.14 0.28
α −π
2
0 0 π
2
0
Joint angles are measured by using a motion capture
system and calculating the resultant quaternion between link
orientations for King Louie. The joint angle for the grub is
measured with an inertial measurement unit IMU located on
the link. King Louie has IMU sensors on each link and we
are purposefully using motion capture data in a way such
that it can be directly replaced by noisier IMU data in future
work.
IV. SINGLE JOINT DYNAMICS AND CONTROL
The steady state joint angle response for a step input of
pressures in both chambers on a single joint shows hysteresis
and depends on the initial pressure and angle states. A
sample mapping can be seen in Figure 3. This mapping was
produced using the same initial conditions for each data point
and varying the commanded pressure in each chamber. The
shape of the map changes when there are different initial
conditions but the trend stays the same. In addition, there are
multiple inputs that map to a single position output which
means there are an infinite number of pressure combinations
to achieve a desired angle. Another complication is that each
pressure combination can result in a steady state response
that varies from the average response by as much as 80◦
due
to hysteresis. In future work, we expect that MPC could take
advantage of having two pressure inputs on each joint and
modeling the hysteresis to have improved performance.
Fig. 3: Plot of resultant steady state angle given specific
initial conditions and two input pressures.
However, in order to simplify the system inputs for initial
controller development, a single pressure profile was used to
achieve a joint angle q. If P0 is the pressure in one actuation
chamber and P1 is the pressure in the other chamber, we let
P0 = 14 + P (1)
and
P1 = 33 − P (2)
with the constraint
1 ≤ P ≤ 18, (3)
the two pressure inputs P0 and P1 are combined into a single
pressure input P which reduces the controller from multi-
input to single input. Figure 4 shows the average angle output
of a single joint for different P values collected with different
initial conditions where the hysteresis can be seen.
Fig. 4: This is a sample pressure profile for a single variable
P.
The trend shown in Figure 4 is the same trend as is seen
in a cross-section of the data shown in Figure 3 and shows
that the input-output relationship closely resembles a sigmoid
function. The equation
θd =
s2
1 + e(−s1(P −s4))
+ s3 (4)
is the general form of the equation we used to map pressure
inputs to input angles for an impedance control model of the
joint used with LQR and MPC. The coefficient s1 determines
the slope of the curve and was fit to match the slope in Figure
4. The coefficients s2, s3, and s4 are determined by the joint
limits. For this work q designates joint angles and θd will
represent system inputs. The system input θd is a function
of pressures that maps the pressure states to an angle which
is necessary for the units in the simplified impedance model,
discussed later, to be consistent.
We have developed this pressure model from data taken
on the grub and have then used it directly on each indi-
vidual joint of King Louie. Our results later in the paper
demonstrate that this model can successfully be used as a
simplified general model for pressure-angle mapping in this
type of robotic system. Although this method simplifies the
control input problem, the constraint on commanded pressure
in the two joint chambers also limits system capabilities. The
ability to use the full pressure range for each bladder could
result in faster system response, better dynamic behaviour,
and the ability to have variable stiffness at each joint.
4. A. Model
For this research we have made several rigid body assump-
tions to simplify the model for the soft robot system such
as there being no lateral or torsional deflection along the
links. Deviations from this approximate model will degrade
overall system performance, but we currently treat them
as disturbances on the system. Future work will include
aspects within the model that capture dynamics unique to
an inflatable robot. However, the results of LQR and MPC
for the system presented here show that a rigid body model
is sufficient for basic control of the system.
The differential equation that we use to describe the
motion of a single link is
I ¨q + Kd ˙q + mg
L
2
sin(q) = τa (5)
where I is the moment of inertia of the link about its joint,
Kd is a damping coefficient, m is the mass of the link, g
is the gravity constant, and L is the length of the link. The
applied torque for our impedance control model is
τa = Ks(θd − q) (6)
where Ks is the stiffness coefficient and θd is the system
input.
This impedance model for torque behaves like a torque
spring where the deflection is the difference between θd and
the joint angles q. θd is assumed to be a steady state response
for a pressure input. Our actual low-level joint pressure
control uses two pressure commands instead of a commanded
angle θd so Equation (4) is needed to map the system input
directly to a pressure combination using Equations (1) and
(2).
Solving for ¨q and linearizing the system with a change of
variable where qe and θe define the point about which we
are linearizing such that
˜q = q − qe (7)
and
˜θd = θd − θe (8)
we can then represent the dynamics in state space form as
follows:
¨˜q
˙˜q
= A
˙˜q
˜q
+ B˜θd (9)
ˆq = C
˙˜q
˜q
(10)
where ˆq is the measurement of the joint angle and
A =
−Kd
I
mg L
2 cos(qe)−Ks
I
1 0
(11)
B =
Ks
I
0
(12)
and
C = 0 1 . (13)
The damping and stiffness terms were estimated directly
by applying a step response to the real system and optimizing
in terms of Kd
I , Ks
I , m. From this system identification, we
saw that forces due to gravity could be modeled as having
little effect on the system output due the low overall weight
of the platform such that:
A ∼=
−Kd
I
−Ks
I
1 0
(14)
was an adequate model.
This was found when m was identified as being near
zero and three orders of magnitude smaller than what our
system identification found for Ks and Kd. Adding a more
significant link-side load to a single joint (or a multi-joint
robot), will require re-examining this approximation in future
work.
With Ts as the time step, we transform the state space
form from the continuous time-domain to the discrete domain
using the matrix exponential method found in [29] where
Ad = eATs
(15)
and
Bd = (Ad − I)A
−1
B (16)
so that the complete discrete state space equation is
˙˜q(k + 1)
˜q(k + 1)
= Ad
˙˜q(k)
˜q(k)
+ Bd
˜θd. (17)
B. Sensing
A single IMU is placed on the link of the grub to
estimate the joint angle. With the grub oriented upwards,
the joint angle can be estimated from the measurements of
two perpendicular accelerometers measuring the direction of
gravity. We use a Kalman filter during actuation to produce
a smoothed state estimate.
C. Control
For the single degree of freedom control development
we used both an LQR and MPC controller for comparison.
Both controllers use the same dynamic model as described
previously in Section IV-A. The form of these two controllers
is described in the next sections. A block diagram for the
system can be seen in Figure 5. The controller sends inputs in
the form of ˜θd which are converted into pressure commands
by solving for P in Equation (4) and then P0 and P1 in
Equations (1) and (2). A PID pressure controller then adjusts
actuator pressures by manipulating electrical currents sent to
spool valves which adjusts air flow. Pressures commanded
to the actuators range from 0 to 17 PSI gage with a source
pressure around 20 PSI gage. The PID gains were tuned for
acceptable performance for the grub and for each joint on
King Louie and can maintain the pressure within 0.1 PSI
from commanded values for P0 and P1. Future hardware
developments will increase the allowable max actuator and
5. source pressure which will result in better response time and
payload capability for the system.
Fig. 5: Control system block diagram for complete system.
1) LQR Control: For position control using a linear
quadratic regulator (LQR), the discrete state space model
from Equation (17) was used. A new discrete state v(k),
defined as
v(k + 1) = v(k) + qgoal(k + 1) − q(k + 1) (18)
which is the discrete approximation of the integral of the
error [29] and qgoal is the commanded joint angle where the
controller is driving the system. The new discrete state space
model then becomes
(19)
˙˜q(k + 1)
˜q(k + 1)
v(k + 1)
= Ai
˙˜q(k)
˜q(k)
v(k)
+ Bi
˜θd
where
Ai =
Ad 0
−CdAd 1
(20)
and
Bi =
Bd
−CdBd
. (21)
In preliminary testing, we saw that without a limit on the
input ˜θd, the controller would command large changes in
inputs that would instantly saturate the pressure controller
in both directions because the pressure dynamics are not
accounted for in the model. In order to limit the system
input, ˜θd was made a state as defined by
˜θd(k + 1) = ˜θd(k) + ∆˜θd (22)
where ∆˜θd was the new system input. The discrete state
space model for the LQR controller then becomes
(23)X(k + 1) = AlqrX(k) + Blqr∆˜θd
where
X = ˙˜q ˜q v ˜θd
T
(24)
Alqr =
Ai Bi
0 1
(25)
and
Blqr = 0 0 0 1
T
. (26)
The control input ∆˜θd was found at each step using the
LQR formulation
∆˜θd = −KX (27)
where K is the solution to the algebraic Riccati equation
using Alqr, Blqr, Q, and R where Q contains the weights
on the states and R is the weight on the input. Both Q
and R were tuned manually so the joint could achieve the
commanded angles.
2) Model Predictive Control: The model predictive con-
troller uses the discrete model Equation (17) to predict future
states across the horizon T. The system is simulated over the
horizon where the inputs ˜θd are picked in such a way that
the cost function is minimized. The cost function minimized
across the horizon is
(28)
f(˜q, ˙˜q, ˜θd) =
T
k=0
˜q(k) − ˜qgoal
2
Q1
+ ˜θd(k) 2
R1
+ ˜θd(k) − ˜θd(k − 1) 2
R2
+ ˜q(T) − ˜qgoal
2
Q2
+ ˙˜q(T) 2
Q3
.
subject to the system model as a constraint in Equation (17)
and the other following constraints:
˜qmin ≤ ˜q ≤ ˜qmax (29)
∆˜θd(k) = ˜θd(k) − ˜θd(k − 1) (30)
∆˜θd ≤ ∆˜θmax. (31)
The values for ˜qmin and ˜qmax are determined by the
current states and the joint limits as follows:
˜qmin = qmin − q (32)
˜qmax = qmax − q. (33)
The limit ∆˜θmax was determined by the slope of the system
response to a step input. This limit is necessary, similar to
LQR, because the pressure dynamics are not currently part
of the system model.
This optimization can be solved at rates up to 200 Hz with
a horizon of T = 65 time steps using C code generated by the
online convex optimization code generation tool CVXGEN
[30]. The first input from the optimized input trajectory is
applied as control to the actual system and the optimization
is then reformulated and runs again at the next time step
with updated states from system measurements. In Equation
6. (28), Q1 weights the error over the horizon, Q2 weights the
final error, Q3 weights the the final velocity, R1 weights
the inputs over the horizon, R2 weights the change in input
over the horizon and all were tuned manually for acceptable
performance.
D. Results and Discussion for Single Joint
Fig. 6: Single Joint Control Response
The MPC response, as well as the LQR responses with and
without an integrator can be seen in Figure 6 for a single
joint. The MPC response shows less overshoot and better
tracking of the goal than the LQR response which for some
inputs relies on the integrator for accurate tracking. However
the LQR response is faster and there is less oscillation around
the goal. The LQR response shows that the system model can
be used for acceptable performance. We expect that further
tuning of the weights for either controller or a dynamic
model that accounts for the pressure dynamics would result
in better performance. Two advantages that MPC has over
LQR is the ability to add constraints, and add different terms
to the cost function without major changes to the system
model.
V. MULTIPLE JOINT DYNAMICS AND CONTROL
As shown previously MPC and LQR are both acceptable
preliminary position control methods for a single degree of
freedom joint. Although, the performance between the two
was fairly comparable, there are pressure saturation limits
and pressure dynamics that are currently not included in
our control formulation. This along with other constraints
(such as joint limits) that are currently included in our MPC
model lead us to expect that MPC will be a better long term
solution than LQR. However we have used both MPC and
LQR methods developed for a single joint to develop and
implement MPC and LQR controllers for King Louie (a 5-
DOF inflatable robot).
A. Model
The same rigid body model that was used for the grub
was used for each joint on King Louie. Each joint is treated
independently from the rest, even though inertia and gravity
have a much greater effect than they did on a single joint.
However, ignoring these effects in the model and treating
them as disturbances does not prevent successful control of
the arm. This is in part due to the low total weight of the
arm which is estimated to be between 10 and 15 pounds.
When compared to the actuator pressures which for this work
have a maximum value of 17 PSI gage, the reaction forces
between the opposing bladders are expected to be orders of
magnitude greater than the forces due to mass and gravity
so the system inputs will have a dominating effect on the
system response.
B. Sensing
For King Louie we used motion capture sensors for
controller development. We calculate the relative quaternion
rotation between frames on each link. Assuming rigid joints
and links, we decompose the quaternion describing the
rotation between two links into the relevant Euler angles.
The motion capture reflective markers used to measure the
joint angles can be seen in Figure 8.
C. Control
Similar to control for the grub, we compared LQR and
MPC for controlling five degrees of freedom simultaneously.
We only control five degrees of freedom because we are
treating the gripper as either opened or closed and we are
not actively controlling the hip joints besides inflating both
of them to maximum pressure to increase stability of the
torso.
For both MPC and LQR, system parameters and weight-
ings were tuned at each joint for acceptable performance.
Each joint is controlled by a separate process and solves
for MPC at 200 Hz for each joint. Because the MPC cost
function can be minimized at 200 Hz both the MPC and
LQR controllers were operating at 200 Hz. A higher level
controller passes desired joint angles to each joint separately.
For this work, only step commands are passed to the con-
trollers. One benefit to this type of controller developed for
a single joint is that MPC can run at faster rates with a
longer time horizon. A single controller that accounts for
all the joints and more of the system dynamics would have
slower solve rates and necessarily shorter horizons because
of computational limitations, severely limiting the low level
controller bandwidth. However, accounting for the low level
control response in a higher level controller for future work,
where high bandwidth is not as important, may be beneficial
for overall control.
D. Results and Discussion for King Louie
We were able to successfully implement MPC and LQR
joint angle control for a multiple joint inflatable robot system.
The system response to a step input sent to each joint can
be seen in Figure 7. In Figures 8a and 8b, King Louie’s arm
can be seen at the commanded angles. This orientation was
chosen to show that King Louie can grab and manipulate
objects in front of itself.
7. Fig. 7: King Louie Step Response
MPC and LQR tracked the commanded joint angles with
similar trends as with one joint. MPC is slower to achieve
the commanded angle but tracks well for all the joints with
some oscillation. LQR has the faster response but does not
track as well especially for Joint 2. This joint was fabricated
differently than the other joints so that motion is restricted
in one direction.
The slope of the sigmoid function in equation (4), could
be manipulated through s1 which was important for stability.
Originally, the coefficients where chosen the match the
profile in Figure 4, but were then tuned for each individual
joint. A steeper slope was necessary for the joints at the
wrist which is likely because these joints are much smaller
than the shoulder joints, and are more sensitive to changes
in pressure. A steeper slope forced the inputs towards the
center of the pressure range for these joints. A more shallow
slope was necessary for the shoulder joints where the full
pressure range was needed to compensate for disturbances
caused by the mass of the arm.
Using MPC, joint space commands were given in a
sequence to perform a task. The task involved picking up a
board from a chair and placing it in a box behind King Louie
(See attached video or https://youtu.be/o044-KW921I). The
board weighs 1.38 pounds (.63 kg), is 20.5 inches (52 cm)
long , 3.5 inches (9 cm) wide, and 1.5 inches (4 cm) thick.
The task was completed eight out of ten times successfully.
For one of the failed attempts, the board bounced off the
top of the box and fell out of the box. For the other
failed attempt, some of the IR reflective markers were not
identified by the motion capture system which caused our
joint estimation to fail after already successfully retrieving
the object but before placing it in the box.
(a) King Louie Arm Down (b) King Louie Arm Up
Fig. 8: Inflatable Systems
VI. CONCLUSIONS
We have successfully demonstrated the viability of a
Linear Quadratic Regulator and Model Predictive Control
in controlling an inflatable humanoid robot. These systems
show great potential for high levels of human-robot interac-
tion due their innate compliance and low inertia. In the future
we expect this type of robot will work together with humans
to perform many tasks. The preliminary work demonstrated
in this paper is a first step towards that goal and towards
improved performance for soft robot control in general.
ACKNOWLEDGEMENT
This work was supported by an Early Career Faculty grant
from NASAs Space Technology Research Grants Program.
We also wish to acknowledge the hard work of Kevin
Albert and his team at Pneubotics (a spinoff of Otherlab
Company) for collaboration in regards to our soft robot
hardware platform.
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