This paper distinguishes between batch mode operations of conventional industrial robots and real-time robot operations that the laser positioning system affords. It introduces the Flexbot, a sub-scale experimental robot for desk top demonstrations, and it introduces a "virtual reality" motion control technique that allows a user to interact with a "virtual robot" on the computer screen to perform real-time robot motion control of the Flexbot.
A pick and place robot with a end effector to grip and place objects in your desired location,controlled by RF communication. Pick and place robot has many advantages and it uses in military, medical and defense applications.
A pick and place robot with a end effector to grip and place objects in your desired location,controlled by RF communication. Pick and place robot has many advantages and it uses in military, medical and defense applications.
Robots can be autonomous, semi-autonomous or
remotely controlled [6].
The robot arm is widely used in many industries and dangerous areas. Automatic control of the robotic
manipulator involves study of kinematic. The kinematic problem is defined as the transformation from the
Cartesian space to the joint space and vice versa This system include the kinematic control which is used for
picking and placing the object in its workspace. There are many types of robot arm in the world of engineering.
This research describes design of jointed robot arm control system using kinematic modelling. The main focus of
this system is to control the end-effector of robot arm to achieve the desired position in the workspace using
MATLAB programming, microcontroller and inverse kinematic modelling. The MATLAB window(GUI) is used
the inverse kinematic for the requirement data for the specified angle of the arm and displayed on the computer.
The description of this system is to implement the hardware components for the moving process and to control
servo motors with pulse width driving circuit. PIC and Max-232 been used to drive for the servo motors of the
control system and receiving serial data from the computer. The control program is written in Mikro-C
programming language.
Modelling of walking humanoid robot with capability of floor detection and dy...ijfcstjournal
Most humanoid robots have highly complicated structure and design of robots that are very similar to
human is extremely difficult. In this paper, modelling of a general and comprehensive algorithm for control
of humanoid robots is presented using Colored Petri Nets. For keeping dynamic balance of the robot,
combination of Gyroscope and Accelerometer sensors are used in algorithm. Image processing is used to
identify two fundamental issues: first, detection of target or an object which robot must follow; second,
detecting surface of the ground so that walking robot could maintain its balance just like a human and
shows its best performance. Presented model gives high-level view of humanoid robot's operations.
In this slides, discussed about basic components of control system, step process of control system design and analysis, open loop and closed loop control system, robot accessories and classification of robot as per application.
Technology, is today, imbibed for accomplishment of several tasks of varied complexity, in almost all walks of life. The society as a whole is exquisitely dependent on science and technology. Technology has played a very significant role in improving the quality of life. One way through which this is done is by automating several tasks using complex logic to simplify the work.Gesture recognition has been a research area which received much attention from many research communities such as human computer interaction and image processing The keyboard and mouse are currently the main interfaces between man and computer. In other areas where 3D information is required, such as computer games, robotics and design, other mechanical devices such as roller-balls, joysticks and data-gloves are used. The main motto of this project is to make robot realize the human gesture, thereby it bridge the gap between robot and human. Human gesture enhances human-robot interaction by making it independent from input devices. Robotic system can be controlled manually, or it may be autonomous. Robotic hand can be controlled remotely by hand gesture. Research in this field has taken place, sensing hand movements and controlling robotic arm has been developed.
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
This paper presents the use of ARDUINO board to control an autonomous mobile robot (AMR) for navigation purpose. The wheeled robot is capable to perform two tasks. The first task is to move autonomously to the north direction. And the second task is to avoid collision with unexpected static and moveable obstacles. The robot uses two sensors to navigate and avoid the obstacle, a digital compass HMC5883L uses to detect the north direction and update the situation of the robot during its movement. And the ultrasonic sensor uses to avoid nearest obstacles on the robot way. C language used to program the system to do its mission and installed to the ARDUINO board. The results obtained show that the robot was able to navigate and move to the north and avoid obstacles in the outdoor environment.
Robots can be autonomous, semi-autonomous or
remotely controlled [6].
The robot arm is widely used in many industries and dangerous areas. Automatic control of the robotic
manipulator involves study of kinematic. The kinematic problem is defined as the transformation from the
Cartesian space to the joint space and vice versa This system include the kinematic control which is used for
picking and placing the object in its workspace. There are many types of robot arm in the world of engineering.
This research describes design of jointed robot arm control system using kinematic modelling. The main focus of
this system is to control the end-effector of robot arm to achieve the desired position in the workspace using
MATLAB programming, microcontroller and inverse kinematic modelling. The MATLAB window(GUI) is used
the inverse kinematic for the requirement data for the specified angle of the arm and displayed on the computer.
The description of this system is to implement the hardware components for the moving process and to control
servo motors with pulse width driving circuit. PIC and Max-232 been used to drive for the servo motors of the
control system and receiving serial data from the computer. The control program is written in Mikro-C
programming language.
Modelling of walking humanoid robot with capability of floor detection and dy...ijfcstjournal
Most humanoid robots have highly complicated structure and design of robots that are very similar to
human is extremely difficult. In this paper, modelling of a general and comprehensive algorithm for control
of humanoid robots is presented using Colored Petri Nets. For keeping dynamic balance of the robot,
combination of Gyroscope and Accelerometer sensors are used in algorithm. Image processing is used to
identify two fundamental issues: first, detection of target or an object which robot must follow; second,
detecting surface of the ground so that walking robot could maintain its balance just like a human and
shows its best performance. Presented model gives high-level view of humanoid robot's operations.
In this slides, discussed about basic components of control system, step process of control system design and analysis, open loop and closed loop control system, robot accessories and classification of robot as per application.
Technology, is today, imbibed for accomplishment of several tasks of varied complexity, in almost all walks of life. The society as a whole is exquisitely dependent on science and technology. Technology has played a very significant role in improving the quality of life. One way through which this is done is by automating several tasks using complex logic to simplify the work.Gesture recognition has been a research area which received much attention from many research communities such as human computer interaction and image processing The keyboard and mouse are currently the main interfaces between man and computer. In other areas where 3D information is required, such as computer games, robotics and design, other mechanical devices such as roller-balls, joysticks and data-gloves are used. The main motto of this project is to make robot realize the human gesture, thereby it bridge the gap between robot and human. Human gesture enhances human-robot interaction by making it independent from input devices. Robotic system can be controlled manually, or it may be autonomous. Robotic hand can be controlled remotely by hand gesture. Research in this field has taken place, sensing hand movements and controlling robotic arm has been developed.
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
This paper presents the use of ARDUINO board to control an autonomous mobile robot (AMR) for navigation purpose. The wheeled robot is capable to perform two tasks. The first task is to move autonomously to the north direction. And the second task is to avoid collision with unexpected static and moveable obstacles. The robot uses two sensors to navigate and avoid the obstacle, a digital compass HMC5883L uses to detect the north direction and update the situation of the robot during its movement. And the ultrasonic sensor uses to avoid nearest obstacles on the robot way. C language used to program the system to do its mission and installed to the ARDUINO board. The results obtained show that the robot was able to navigate and move to the north and avoid obstacles in the outdoor environment.
Real Time Robot Control System Software Description_3
1. Real Time Robot Control System Software Description
1
Contents
Real Time Robot Motion Control................................................................................... 1
How to Manipulate the Virtual Robot Geometrical Positions........................................ 3
2D Real-Time Motion Control Software.................................................................... 3
3D Real Time Robot Control System Software.......................................................... 6
How to Control the Virtual Robot’s Hardware (2D mode) ............................................ 7
Real Time Robot Motion Control
Conventional industrial robots are programmed to operate independently of human
interaction in batch operations. In batch operations an industrial robot executes a fixed
prewritten program to perform some operation such as, machining, welding and painting.
The batch mode is similar to what (Computerized Numerical Control) CNC machines do
to make parts using “G-code” as a prewritten program.
Real time motion control enables a robot to be trained to perform operations such as
painting the exterior of a house or performing surgeries without relying upon prewritten
programs. The user controls a virtual robot on the computer screen using a pointer in
computer space (such as a mouse) or physical space (such as a 3D laser distance meter).
The actual robot follows the virtual robot’s operations.
2. Real Time Robot Control System Software Description
2
Figure 1 Flexbot Real-Time Motion Control System
Flexbot (Figure 1) is a sub-scale experimental real time robot motion control system
under development. Flexbot introduces a “virtual reality” motion control technique. The
user interacts with a “virtual” robot on the computer screen: 1) a virtual robot trains the
user to maneuver the robot geometry in various modes 2) the virtual robot predicts how
the actual robot will operate before physical motion occurs. In the appropriate software
mode, the actual robot follows the virtual robot motions from the computer memory.
Flexbot is comprised of computer software, sub-scale robot hardware, and servo drive
electronics. The virtual robot motion (i.e. motion that is expected) and the predicted robot
motion (the hardware motion in physical space) are compared on the screen.
Flexbot’s software can be used in the classroom to train students in control theory (such
as proportional, integral, and derivative (PID) gain settings) and to beta test real time
robot motion control methodologies under development. Both activities help instructors
to teach the engineering design process to students.
The Lego Mindstorms education products and MATLAB hardware in the loop (HIL)-
Simulink development systems offer similar, but incomplete tools to implement the real
time robot motion control system.
Demo software for the real time robot motion control software that is discussed in this
paper can be found at this link:
http://www.laserpositioningsystem.com/Virtual_Robot_Demo/ArmCmdSim.zip
3. Real Time Robot Control System Software Description
3
A description of how to manipulate the “virtual robot’s geometrical positions follows,
after which, a description of the how to control “virtual robot” hardware with PID
controls settings is presented.
How to Manipulate the Virtual Robot Geometrical Positions
The Flexbot is a sub-scale prototype jointed-arm PC controlled robot.. Each of the
Flexbot’s drive motors are stationary to reduce the inertia of the arm’s moving parts. The
arm has two degrees of freedom in movement. The Flexbot is designed to demonstrate a
virtual reality motion control technique under development. A mouse is used to control a
virtual robot on a PC’s computer screen. The virtual robot’s position in the computer’s
memory is used to control the motions of the Flexbot hardware. The user points to a
desired location on the screen. The virtual robot follows. In the appropriate software
mode, the real sub-scale robot hardware follows the virtual robot by converting the digital
information to analog commands. The Figure 2 is a block diagram of Flexbot’s software
and hardware interfaces:
Flexbot Graphical User
Interface Software
Inverse
Kinematics
Program
Robot Motion
Control
Module
Sequential Joint
Cartesian
Coordinate
Polar Coordinate
Dual Axis
Computer
Display
Screen Mode
Screen and
Hardware Mode
DAC
Absolute Position
Laeer Guided
Flexbot Motion Control Modes
Figure 2 Flexbot Software and Hardware Interfaces
2D Real-Time Motion Control Software
Flexbot Software Operation Procedures (Screen Mode)
The Flexbot’s graphical user interface (GUI) software has five modes of motion control:
1. Sequential Joint
2. Cartesian coordinates
3. Polar coordinates
4. Dual Axis Control
5. Absolute Position
Select the particular motion mode from the pull down menu in the upper left hand corner
of the window. The Trajectory Mode dialog box on the upper right hand side of the
window displays the selected Trajectory Mode. The default Trajectory Mode is
“Sequential Joint Motion Control”. See Figure 3 below
4. Real Time Robot Control System Software Description
4
Figure 3 Screenshot of Flexbot’s Graphical User Interface with Trajectory Modes Shown
5. Real Time Robot Control System Software Description
5
Procedures for Each Control Mode:
1. Sequential Joint Motion Control (each joint is moved in sequence)
a. Humerus or Shoulder Joint
1. With the mouse cursor placed anywhere within the
window, press and hold left mouse button. Move the mouse
in the horizontal direction (left or right)
b. Radius or Elbow Joint
1. With the mouse cursor placed anywhere within the
window, press and hold the right mouse button. Move the
mouse in the horizontal direction (left or right)
2. Cartesian Coordinate Motion Control
a. X-axis motion
1. With the mouse cursor placed anywhere within the
window, press and hold left mouse button. Move the mouse
in the horizontal direction (left or right)
b. Y-Axis motion
1. With the mouse cursor placed anywhere within the
window, press and hold right mouse button. Move the
mouse in the vertical direction (up or down)
3. Polar Coordinate Motion Control
a. Angular motion
1. With the mouse cursor placed anywhere within the
window, press and hold left mouse button. Move the mouse
in the horizontal direction (left or right)
b. Radial motion
1. With the mouse cursor placed anywhere within the
window, press and hold right mouse button. Move the
mouse in the horizontal direction (left or right)
4. Dual Axis Motion Control
a. With mouse cursor button placed anywhere on the screen, press and hold
left mouse button. Move mouse cursor up, down, left, and right
5. Absolute Position Motion Control
a. With mouse cursor button placed anywhere on the screen, press and hold
left mouse button. Move mouse cursor up, down, left, and right. End point
of virtual robot sticks to mouse cursor.
6. Real Time Robot Control System Software Description
6
3D Real Time Robot Control System Software
A YouTube video gives an overview of 3D Real-time operations at the following link:
http://youtu.be/gyHMvCit-Ng
Demo software of the 3D real-time robot control system for a typical six-degree of
freedom robot can be found at this link:
http://laserpositioningsystem.com/Virtual_Robot_Demo/Self_Loading_6DOFRobot_Soft
ware.zip
Video instructions of how to use the 3D virtual robot software can be found at this link:
http://www.laserpositioningsystem.com/Virtual_Robot_Demo/6DOF_Demo3.html
The 3D “virtual robot” video shows a virtual representation of a three dimensional Laser
Positioning System (LPS) can be found at these links:
http://laserpositioningsystem.com/images/
http://laserpositioningsystem.com/wp-content/uploads/2014/12/How-to-use-the-Laser-
Positioning-System.pdf
7. Real Time Robot Control System Software Description
7
How to Control the Virtual Robot’s Hardware (2D mode)
Figure 4 Flexbot Angular Position and Velocity Feedback Electronics
The robot real time hardware includes a controller, controller optimization parameter
adjustments, a servo amplifier, motor electrical and mechanical time constants, motor
angular position and angular velocity feedback, motor gear box. Figure 4 (above) shows
the Flexbox angular position and velocity feedback electronics. The Flexbot
instrumentation in Figure 4 includes high resolution encoders with quadrature decoders to
keep track of angular positions. The quadrature decoders are part of the Encoder Counter
Unit (ECU). The electronics include frequency to voltage converters that are integrated
into the Encoder Tachometer Unit (ETU) to detect the motor encoder’s shaft speed.
Figure 5 (below) shows a block diagram of the real time robot motion control system.
Arm Movement Mechanism
The following paragraph is brief description of how the Flexbot robot arm moves. The
Flexbot has two primary structural members:1) the humerus 2) and the radius. The
humerus attaches to the shoulder joint (the main shaft mounted on the pillow blocks). The
radius attaches at the elbow joint (the outer most pulley that is mounted at the opposite
end of the humerus). There are three belts that are mounted to the main shaft. The top
most belt rotates the humerus member. The bottom two belts rotate the radius member
using an idler pulley on the main shaft as an intermediate step.
8. Real Time Robot Control System Software Description
8
Development Status
Controlling the Flexbot hardware is the third step in using the real time robot control
system. A new version of the motor speed feedback loop is now being implemented in
experiments.
Digital to
Analog
Converter
(DAC)
Servo Amplifier
PC
Graphical User
Interface
Motor Position
Feedback
Motor Speed
Feedback
Motor Position
Feedback
Motor Speed
Feedback
Electric Motor Gearbox shaft
Electric Motor Gearbox shaft
Real Time Robot Control System Block Diagram
PID Control
Figure 5 Real Time Robot Motion Control System Block Diagram
Using Flexbot’s Real Time Motion Control Software to Control its Hardware
To operate both the virtual robot and the hardware at the same time, choose the “Object”
tab at the top of the window. Use your mouse to select the “Screen and Hardware” option
in the pull down menu. See Figure 6 below.
9. Real Time Robot Control System Software Description
9
Figure 6 Making both the Screen and Hardware Move
More text boxes appear in the window to assist in tuning the hardware for the best
response to a virtual robot position change. See Figure 5 below. The upper row of
parameters is for the humerus member of the robot’s arm. The lower row of parameters is
for the radius member (forearm) of the robot’s arm. The dark gray image is a
representation of the location of the robot hardware. The “Angle Error” is an important
measure of the hardware’s (i.e. dark gray image’s) ability to follow the lead of the red
virtual (screen) robot.
The last three columns at the top of the window are proportional, integral, and derivative
(PID) adjustment factors to optimize the hardware response to the red robot motions. For
10. Real Time Robot Control System Software Description
10
example, the default PID factors are set at values that give the hardware a rapid response
to the red virtual robot.
If you adjust the proportional gain factor, “Kpro”, to “5” instead of the default “1000”
value, the (dark gray) virtual robot hardware will respond very slowly by lagging behind
the (red) virtual (screen) robot.
The integral gain, “Kint” factor is best set at a value near 0.0000001. Values appreciably
above this will cause the hardware to become unstable and unable to follow the lead of
the red virtual robot.
Changing the Derivative gain factor,” Kdrv” to 0 will cause the hardware to oscillate or
overshoot the red virtual robot target position.
11. Real Time Robot Control System Software Description
11
Figure 7, Virtual (red) and Simulated Physical Hardware (gray) Diagrem
12. Real Time Robot Control System Software Description
12
Flexbot’s Mechanical Limits of Rotation
Figure 8 Flexbot’s Radius and Humerus Mechanical Limits of Rotation
Figure 9 Flexbot’s Radius Mechanical Limit of Rotation
The concepts introduced in this version of a real time robot control system are protected
by US Patent No. 9,044,857