A presentation that explores the possibility of the Elastic Band construct, which combines controllers with motion planning for more dynamic robot motion
Geotechnical Engineering-II [Lec #24: Coulomb EP Theory]Muhammad Irfan
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
Geotechnical Engineering-II [Lec #25: Coulomb EP Theory - Numericals]Muhammad Irfan
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
Pseudo Spectral Optimal Control for Coverage Path PlanningDebasmit Das
This is project presentation for AAE-568 (Optimal Control) at Purdue University. The whole Project Report is available at https://arxiv.org/abs/1708.03055
Heroku's Ryan Smith at Waza 2013: Predictable FailureHeroku
Heroku's Ryan Smith took to the Waza 2013 stage to present "Predictable Failure" -- a look at how engineers can prepare for the inevitable. For more from Smith ping him at @ryandotsmith.
For Waza videos stay tuned at http://blog.heroku.com or visit http://vimeo.com/herokuwaza
Geotechnical Engineering-II [Lec #24: Coulomb EP Theory]Muhammad Irfan
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
Geotechnical Engineering-II [Lec #25: Coulomb EP Theory - Numericals]Muhammad Irfan
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
Pseudo Spectral Optimal Control for Coverage Path PlanningDebasmit Das
This is project presentation for AAE-568 (Optimal Control) at Purdue University. The whole Project Report is available at https://arxiv.org/abs/1708.03055
Heroku's Ryan Smith at Waza 2013: Predictable FailureHeroku
Heroku's Ryan Smith took to the Waza 2013 stage to present "Predictable Failure" -- a look at how engineers can prepare for the inevitable. For more from Smith ping him at @ryandotsmith.
For Waza videos stay tuned at http://blog.heroku.com or visit http://vimeo.com/herokuwaza
Some experiments done on MATLAB for the course on Simulation and Modelling. Includes Model of Bouncing Ball, Model of Spring Mass System and Model of Traffic Flow.
This PPT gives information about:
1. Robotics: History, Concepts, principles and applications of Robots, different types of Robots, degrees of freedom. Robot classification. Robotic vision. Controlling robot movements.
2. Robotics Hardware:
3. Sensors: IR sensors, Proximity Sensor, Ultrasonic Sensor, White line sensor, Temperature Sensor, Touch sensor, Tilt Sensor, Accelerometer, Gyroscopic Sensor etc.
4. Actuators: DC motor, Servo motor, Stepper Motor, Motor driver ICs, Half Bridge & Full bridge circuits, Pulse Width Modulation and Gripper.
6. Controller: Architecture and features of ATMEGA16 (in detail) other higher versions of AVR Microcontrollers..
Exposing Architecture in Hybrid ProgramsIvan Ruchkin
The formalism of hybrid programs is a relatively recent development by Platzer et al. (CSD) that allows holistic reasoning about discrete and continuous behavior within the same model. This makes hybrid modeling and verification, supported by tools like KeYmaera, an attractive method for analyzing cyber-physical systems. However, hybrid programs do not operate architectural concepts, making it difficult to relate them between each other and other kinds of CPS models. My talk introduces architectural annotations for hybrid programs that allow engineers to express the otherwise implicit architectural knowledge. This presentation discusses annotation primitives and their interpretation.
robotics presentation (2).ppt is good for the student life and easy to gain t...poojaranga2911
power point of robotics
Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. Within mechanical engineering, robotics is the design and construction of the physical structures of robots, while in computer science, robotics focuses on robotic automation algorithms.
Experimental Comparison of Trajectory Planning Algorithms for Wheeled Mobile ...IJRES Journal
In this paper, we present an experimental approach to compare various trajectory planning methods for practical application of wheeled mobile robots. The first method generates a trajectory according to the acceleration limits of the mobile robot and its relationship with the curvature of the planned path. The second method is an improvement of the conventional convolution-based trajectory generation method, on which the heading angles of a curved path is being considered. Due to the limited scope of the considered constraints of the previous approaches, A third approach that conserves the merits of the convolution operator is proposed to consider the high curvature turning points of a sophisticated curve such as a Lemniscate of Gerono,which causes geometrical limitations during robot navigation. All methods are compared experimentally on a two-wheeled mobile robot. The goal of the experiment is to determine which approach meets the criteria of time optimality and sampling time uniformity while considering the physical limits of the mobile robot and the geometrical constraints of the planned path.
15_robotics-intro.pdf in ai machine learningMcSwathi
this is artificial inelligence robotics machine learningArtificial intelligence and robotics are very recent technologies and risks for our world. They are
developing their capacity dramatically and shifting their origins of developing intention to other
dimensions. When humans see the past histories of AI and robotics, human beings can examine and
understand the objectives and intentions of them which to make life easy and assist human beings within
different circumstances and situations. However, currently and in the near future, due to changing the
attitude of robotic and AI inventors and experts as well as based on the AI nature that their capacity
of environmental acquisition and adaptation, they may become predators and put creatures at risk.
They may also inherit the full nature of creatures. Thus, finally they will create their new universe
or the destiny of our universe will be in danger.Artificial intelligence describes the work processes of machines that would require intelligence
if performed by humans (Wisskirchen et al., 2017). The term ‘artificial intelligence’ thus means
‘investigating intelligent problem-solving behavior and creating intelligent computer systems.
There are two kinds of artificial intelligence:
• Weak Artificial Intelligence: the computer is merely an instrument for investigating cognitive
processes – the computer simulates intelligence.
• Strong Artificial Intelligence: The processes in the computer are intellectual, self-learning
processes. Computers can ‘understand’ by means of the right software/programming and are able
to optimize their own behavior on the basis of their former behavior and their experience. 4 This
includes automatic networking with other machines, which leads to a dramatic scaling effect.
According to the Robot Institute of America (1979) a robot is: “A reprogram able, multi-
functional manipulator designed to move material, parts, tools, or specialized devices through
various programmed motions for the performance of a variety of tasks” (Bansal et al., 2017). A
more inspiring definition can be found in Webster. According to Webster a Robot is: “An automatic
device that performs functions normally ascribed to humans or a machine in the form of a human.”
A robot can be defined as a programmable, self-controlled device consisting of electronic, electrical,
or mechanical units. More generally, it is a machine that functions in place of a living agent. Robots
are especially desirable for certain work functions because, unlike humans, they never get tired; they
can endure physical conditions that are uncomfortable or even dangerous; they can operate in airless
conditions; they do not get bored by repetition; and they cannot be distracted from the task at hand.
The birth of the computer took place when the first calculator machines were developed, from the
mechanical calculator of Babbage, to the elector-mechanical experts
This PPT gives information about:
1.Practical building simple wheeled mobile robots
2. Timeline
3. Classification
4. Robot Accessories
5. Robot Configuratin
6. Control Methods
Yelp Data Challenge - Discovering Latent Factors using Ratings and ReviewsTharindu Mathew
A restaurant's average rating and reviews on Yelp in influence customers to an incredible degree. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently. Despite the impact on the restaurant's business, achieving a better overall rating is not straightforward. A user may give only one star to the restaurant just because he or she found the quality of service to be abysmal even though the food and the restaurant's location were up to his or her standard. These facts may have been mentioned in the review in detail but the final rating would just reflect the poor quality of service. The user rating alone does not provide any additional details, and as a result, the restaurant may not be able to understand which aspects create a negative impact on user experience. Another case may be that a certain popular dish will make users give the restaurant five star ratings, but they would not be satisfied with another aspect of the restaurant such as the dessert. The high user ratings may hide the fact that some aspects of the user experience was negative and that the restaurant has room to improve. Traditional recommender systems usually use only the aggregated ratings without considering the hidden factors in the preference of the users and the properties of the restaurants. For the restaurant domain, this could mean main cuisine, dessert, service, staff friendliness, knowledge of staff, location, ambiance, price and many more aspects. Without considering the ratings for individual aspects, it is likely that the recommendation systems will give inaccurate predictions to restaurants as well as users.
In this project, we aim to uncover hidden details about the users' preferences with respect to restaurant properties. With this information, we can provide precise recommendations to the restaurants regarding what aspects they should concentrate on to improve user experience. Since we are backed by more meaningful information about users' preferences we can provide better recommendations to users as to which restaurants they would prefer and why. To summarize, from the results of this project, we can answer the following questions: "what does a particular user care about when dining from a restaurant?", "which aspect should the restaurant improve in order to effectively increase the rating?", and "which restaurant is the best for a particular user?"
3DRSim is a sandbox that allows you to load 3D models, and use projected structured light to create 3D reconstructions of those objects.
A single-shot colored structured light method based on a Debruijn sequence is implemented. This particular algorithm is based on the research by Zhang et al in 2002 [1]. The paper is available at [2].
[1] - Li Zhang, Brian Curless, and Steven M. Seitz - Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming
[2] - http://grail.cs.washington.edu/projects/moscan/paper.pdf
More Related Content
Similar to Combining PID controllers with Robot Motion Planning
Some experiments done on MATLAB for the course on Simulation and Modelling. Includes Model of Bouncing Ball, Model of Spring Mass System and Model of Traffic Flow.
This PPT gives information about:
1. Robotics: History, Concepts, principles and applications of Robots, different types of Robots, degrees of freedom. Robot classification. Robotic vision. Controlling robot movements.
2. Robotics Hardware:
3. Sensors: IR sensors, Proximity Sensor, Ultrasonic Sensor, White line sensor, Temperature Sensor, Touch sensor, Tilt Sensor, Accelerometer, Gyroscopic Sensor etc.
4. Actuators: DC motor, Servo motor, Stepper Motor, Motor driver ICs, Half Bridge & Full bridge circuits, Pulse Width Modulation and Gripper.
6. Controller: Architecture and features of ATMEGA16 (in detail) other higher versions of AVR Microcontrollers..
Exposing Architecture in Hybrid ProgramsIvan Ruchkin
The formalism of hybrid programs is a relatively recent development by Platzer et al. (CSD) that allows holistic reasoning about discrete and continuous behavior within the same model. This makes hybrid modeling and verification, supported by tools like KeYmaera, an attractive method for analyzing cyber-physical systems. However, hybrid programs do not operate architectural concepts, making it difficult to relate them between each other and other kinds of CPS models. My talk introduces architectural annotations for hybrid programs that allow engineers to express the otherwise implicit architectural knowledge. This presentation discusses annotation primitives and their interpretation.
robotics presentation (2).ppt is good for the student life and easy to gain t...poojaranga2911
power point of robotics
Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. Within mechanical engineering, robotics is the design and construction of the physical structures of robots, while in computer science, robotics focuses on robotic automation algorithms.
Experimental Comparison of Trajectory Planning Algorithms for Wheeled Mobile ...IJRES Journal
In this paper, we present an experimental approach to compare various trajectory planning methods for practical application of wheeled mobile robots. The first method generates a trajectory according to the acceleration limits of the mobile robot and its relationship with the curvature of the planned path. The second method is an improvement of the conventional convolution-based trajectory generation method, on which the heading angles of a curved path is being considered. Due to the limited scope of the considered constraints of the previous approaches, A third approach that conserves the merits of the convolution operator is proposed to consider the high curvature turning points of a sophisticated curve such as a Lemniscate of Gerono,which causes geometrical limitations during robot navigation. All methods are compared experimentally on a two-wheeled mobile robot. The goal of the experiment is to determine which approach meets the criteria of time optimality and sampling time uniformity while considering the physical limits of the mobile robot and the geometrical constraints of the planned path.
15_robotics-intro.pdf in ai machine learningMcSwathi
this is artificial inelligence robotics machine learningArtificial intelligence and robotics are very recent technologies and risks for our world. They are
developing their capacity dramatically and shifting their origins of developing intention to other
dimensions. When humans see the past histories of AI and robotics, human beings can examine and
understand the objectives and intentions of them which to make life easy and assist human beings within
different circumstances and situations. However, currently and in the near future, due to changing the
attitude of robotic and AI inventors and experts as well as based on the AI nature that their capacity
of environmental acquisition and adaptation, they may become predators and put creatures at risk.
They may also inherit the full nature of creatures. Thus, finally they will create their new universe
or the destiny of our universe will be in danger.Artificial intelligence describes the work processes of machines that would require intelligence
if performed by humans (Wisskirchen et al., 2017). The term ‘artificial intelligence’ thus means
‘investigating intelligent problem-solving behavior and creating intelligent computer systems.
There are two kinds of artificial intelligence:
• Weak Artificial Intelligence: the computer is merely an instrument for investigating cognitive
processes – the computer simulates intelligence.
• Strong Artificial Intelligence: The processes in the computer are intellectual, self-learning
processes. Computers can ‘understand’ by means of the right software/programming and are able
to optimize their own behavior on the basis of their former behavior and their experience. 4 This
includes automatic networking with other machines, which leads to a dramatic scaling effect.
According to the Robot Institute of America (1979) a robot is: “A reprogram able, multi-
functional manipulator designed to move material, parts, tools, or specialized devices through
various programmed motions for the performance of a variety of tasks” (Bansal et al., 2017). A
more inspiring definition can be found in Webster. According to Webster a Robot is: “An automatic
device that performs functions normally ascribed to humans or a machine in the form of a human.”
A robot can be defined as a programmable, self-controlled device consisting of electronic, electrical,
or mechanical units. More generally, it is a machine that functions in place of a living agent. Robots
are especially desirable for certain work functions because, unlike humans, they never get tired; they
can endure physical conditions that are uncomfortable or even dangerous; they can operate in airless
conditions; they do not get bored by repetition; and they cannot be distracted from the task at hand.
The birth of the computer took place when the first calculator machines were developed, from the
mechanical calculator of Babbage, to the elector-mechanical experts
This PPT gives information about:
1.Practical building simple wheeled mobile robots
2. Timeline
3. Classification
4. Robot Accessories
5. Robot Configuratin
6. Control Methods
Yelp Data Challenge - Discovering Latent Factors using Ratings and ReviewsTharindu Mathew
A restaurant's average rating and reviews on Yelp in influence customers to an incredible degree. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently. Despite the impact on the restaurant's business, achieving a better overall rating is not straightforward. A user may give only one star to the restaurant just because he or she found the quality of service to be abysmal even though the food and the restaurant's location were up to his or her standard. These facts may have been mentioned in the review in detail but the final rating would just reflect the poor quality of service. The user rating alone does not provide any additional details, and as a result, the restaurant may not be able to understand which aspects create a negative impact on user experience. Another case may be that a certain popular dish will make users give the restaurant five star ratings, but they would not be satisfied with another aspect of the restaurant such as the dessert. The high user ratings may hide the fact that some aspects of the user experience was negative and that the restaurant has room to improve. Traditional recommender systems usually use only the aggregated ratings without considering the hidden factors in the preference of the users and the properties of the restaurants. For the restaurant domain, this could mean main cuisine, dessert, service, staff friendliness, knowledge of staff, location, ambiance, price and many more aspects. Without considering the ratings for individual aspects, it is likely that the recommendation systems will give inaccurate predictions to restaurants as well as users.
In this project, we aim to uncover hidden details about the users' preferences with respect to restaurant properties. With this information, we can provide precise recommendations to the restaurants regarding what aspects they should concentrate on to improve user experience. Since we are backed by more meaningful information about users' preferences we can provide better recommendations to users as to which restaurants they would prefer and why. To summarize, from the results of this project, we can answer the following questions: "what does a particular user care about when dining from a restaurant?", "which aspect should the restaurant improve in order to effectively increase the rating?", and "which restaurant is the best for a particular user?"
3DRSim is a sandbox that allows you to load 3D models, and use projected structured light to create 3D reconstructions of those objects.
A single-shot colored structured light method based on a Debruijn sequence is implemented. This particular algorithm is based on the research by Zhang et al in 2002 [1]. The paper is available at [2].
[1] - Li Zhang, Brian Curless, and Steven M. Seitz - Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming
[2] - http://grail.cs.washington.edu/projects/moscan/paper.pdf
A Robot powered by a Raspberry Pi, is built and used as a dynamic moving projector for Appearance Editing work.
This was developed at the CGVLab at Purdue University for ongoing Appearance Editing research.
The feedback cycle in the context of APIs talks about gathering API data, slice and dicing this data to gather information, deciding any actions and tuning API parameters. There are considerations for each step, and these actions can easily be implemented through WSO2 BAM
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
2. Robot Motion Planning
● Gives us a path between a starting point and a finish point
● Acts on a model of the world
● Fast planning algorithms that are of practical use exist but computationally
expensive for the general case
● Possible to plan for an optimal path
● Unable to account for changes in the environment
3. PID Controllers
● Application of Control Theory
● Allows to act on feedback received from sensors and react
Source: Wikipedia
4. PID Controllers (cont.)
● Reacts to changes in the real environment
● Cannot achieve a global goal like moving from A to B, only local goals can
be achieved
5. Elastic Bands (Quinlan, Khatib ‘93)
The path from the planner is deformed in real time to handle local changes in
the environment detected by sensors and to smooth the path.
Contraction force and
repulsion force applied
Source: http://cm.bell-labs.com/who/seanq/icra93.pdf
6. Is curve collision free?
Difficult to compute for 2 reasons
● Even for simple planar robot the boundary of the free space is 3 D
● We have to check that curves rather than line segments are in the free
space
7. Bubbles
● Instead of trying to compute and represent the entire free space, a model
of the environment and robot is used to generate, on the fly, local subsets
of the free space
● Each subset, called a bubble, is computed by examining the local freedom
of the robot at a given configuration
Source: http://cm.bell-labs.com/who/seanq/icra93.pdf
8. Defining a bubble
● The function ρ(b) that gives the minimum distance between the robot at
configuration b and the obstacles in the environment.
● B(b) = {q : || b−q || < ρ(b) } - The subset B(b) is labeled the bubble at b.
Source: http://cm.bell-labs.com/who/seanq/icra93.pdf
9. Bubbles for Higher Dimensions
● Robot described by b = (x, y, ϴ)
● rmax - maximum distance from the origin of the robot to any other point on
the robot
● If robot moves from configuration b to b’ we can bound D(b-b’)
10. Manipulating the Bubbles
● The overall strategy for deforming the elastic band is to scan up and down
the sequence of bubbles, moving each in turn.
● Compute an artificial force consisting of internal contraction force and
external repulsive force
Source: http://cm.bell-labs.com/who/seanq/icra93.pdf
11. The internal contracting force
kc- global contraction gain
● Internal contraction models the forces in a physical elastic band
● The force of a bubble at bi is given by
● The interpretation is a series of springs between the bubbles
● The force from each spring is normalized to reflect a uniform tension
12. The repulsive force
● The repulsive forces pushes the bubbles away from the obstacles
● The size of a bubble gives an indication of how far the robot is from
collision. So it’s defined so that it increases this size
kr- global repulsion gain
𝝆0- distance upto which force
is applied
h- step size set to 𝝆(b)
13. New position of the bubble
● After computing the total force, compute the new position of the bubble
𝜶- one possible value is 𝝆(bold)
14. Conclusion
● Elastic bands form a basis for real-time collision free motion control
● Still, to determine a bubble of free space, we need to determine the
distance from each link to the obstacle
● Real time will depend on number of bubbles and processor speed. As
distance of all bubbles have to be calculated around a speed of 10 Hz.