1. Web Enabled Robot Design and Dynamic Control Simulation Software Solutions From Task Points Description Tarek Sobh, Sarosh Patel and Bei Wang School of Engineering University of Bridgeport

2. Table of Content Research Summary Task Point Description Theory The Software Package Results Conclusion Future Development Current Project Status

3. Research Summary A web-based solution for robot design and dynamic control simulation based on given task point descriptions The software combines and utilizes the computational power of both the Mathematica and Matlab packages

4. Research Summary (cont.) Given the location and velocity of each task point, our approach formulates the complete design of a 3 DOF robot model by computing its optimal dynamic parameters such as link length, mass and inertia Suggests the optimal control parameters (Kp, Kv) for the dynamic control simulation Puma560: 3 DOF robot

5. Task Point Description A set of desired positions of an end-effector Velocities at a particular instant of time Problem definition: to obtain the optimal robot design and dynamic control strategy in such a way that the task can be carried out with maximum manipulability and minimum error in reaching the desired positions and velocities

6. Theory Manipulability The Cost Function Optimizing the Cost Function Calculations of Dynamic Parameters Trajectory Generation PD Control Loop Optimization of Kp and Kv

7. Theory Manipulability Manipulability: the ability of the manipulator to accelerate in all directions from that point Yoshikawa:

8. Theory The Cost Function The criteria used to form the cost function: Manipulability Accuracy Distance from the point.  K is the DH parameter of the robot  q1,q2..qm are the joint vectors of the task points

9. Theory Optimizing the Cost Function Uses the steepest descent algorithm, which finds the minima by searching in the direction opposite, to the gradient Minimizing the function provides the optimal values for the DH table

10. Theory Calculations of Dynamic Parameters Calculates manipulator DH table on the following assumptions:  The manipulator links are solid and cylindrical in shape  All links have uniform density (uniform mass distribution)  All the links are made of the same material  There are a finite number of actuators and sensors with known specifications that can be used in the design

11. Theory Calculations of Dynamic Parameters (Cont.) Mass: Center of Gravity: The center of gravity is calculated geometrically with respect to the link coordinate frame

12. Theory Calculations of Dynamic Parameters (Cont.) Inertia: Since the links are considered to be cylindrical, the Inertia about the axis of a cylinder is given by: Using the perpendicular axis theorem the Inertia along the other two axes is given by:

13. Theory Trajectory Generation A seven-degree polynomial to generate the trajectory The control loop is implemented over to support this trajectory

14. Theory PD Control Loop It is advantageous to use a PD control loop:  Simple to implement  Involves few calculations; ideal for real time control provided with optimum Kp and Kv  System behavior can be controlled by changing the feedback gains  Can be implemented in parallel for each link

15. Theory PD Control Loop (cont.) Torque to be applied to the manipulator: Forward Dynamics The feedback loop: Inverse Dynamics In the case of real time control: the sensors provide the feedback

16. Theory PD Control Loop (cont.) a block diagram commonly found in robot prototyping research

17. Theory PD Control Loop (cont.) Kp: proportional gain Kv: derivative gain e: error in position e’: error in velocity

18. Theory Optimization of Kp and Kv Sum of the Square of Errors about the desired trajectory should be less than a specified threshold

19. The Software Package Web Interface Kinematic Design Module Dynamic Design Module Dynamic Control Simulation Module

20. The Software Package (Cont.)

21. Web Interface JSP, Servlet, JLink and JMatservlet Central control module

22. Kinematic Design Module Generate best kinematics robot configuration with max manipulability Modified kinematics synthesis package build on top of Robotica Input: set of task points description Output: a robot configuration in the form of DH table (optimal kinematics properties of the three-link robot)

23. Kinematic Design Module (Cont.) DesignRobot [task_points, configuration, precision, file_name] Task_points: a matrix with xyz coordinates of task points Configuration: a string of ‘R’s and ‘P’s describing prismatic or rotational joints File_name: the location in which the DH configuration file is stored

24. Dynamic Design Module Input: file (DH table) generated by Kinematic model; radii of the links; (mass of the links is pre-assumed) Output: Dynamic parameter matrix ‘dyn’ Running in the MATLAB environment

25. Structure of the DYN matrix

26. Dynamic Control Simulation Module MATLAB environment Input: coordinates of points with respect to a time frame and velocities at those points; specified range of values for Kp and Kv and the step increment Output: optimum value of Kp and Kv, and update frequency

27. Results User login Screen sample run video

28. User specifies number of task points

29. User specifies the coordinates and velocities of each task points with respect to a time scale

30. User specifies link radii for dynamic model generation, and Kp, Kv initialization for dynamic PD control simulation

31. DH table, Dynamic Parameter Matrix and optimal Kp, Kv values for each link

32. A standard PPP model

33. Desired Trajectory for link 1, 2, 3 Desired Vs. obtained link displacement for link 1

34. Desired Vs. obtained link displacement for link 2 Desired Vs. obtained link displacement for link 3

35. Desired velocity trajectory for link 1, 2 and 3 Desired Vs. Obtained velocity for link 1

36. Desired Vs. Obtained velocity for link 2 Desired Vs. Obtained velocity for link 3

37. Conclusion Web-enabled Generates the basic configuration of a manipulator based on user specified task points, in order to attain the greatest manipulability in the workspace. Provides the optimum values of Kp, Kv for optimum dynamic control.

38. Future Development Building better cost functions Customizable objective functions Advanced trajectory generation algorithms Faster algorithms for calculation of inverse kinematics A numerical solution package for inverse kinematics for a few common robot models Implementation of PID control in addition to PD control, to further minimize the error

39. Current Project Status The following paper: A MOBILE WIRELESS AND WEB BASED ANALYSIS TOOL FOR ROBOT DESIGN AND DYNAMIC CONTROL SIMULATION FROM TASK POINTS DESCRIPTION has been accepted by the Journal of Internet Technology

40. References Proceedings: Lloyd J., Hayward V. “A Discrete Algorithm for Fixed-path Trajectory Generation at Kinematic Singularities”, IEEE Int. Conf. on Robotics and Automation, Minneapolis (1996) Proceedings: Sobh T. and Toundykov D. “Kinematic Synthesis of Robotic Manipulators from Task Descriptions”, to appear in IEEE magazine on Robotics and Automation, summer (2003). Journal: Yoshikawa T. “Manipulability of Robot Mechanisms”. International Journal of Robotics Research, vol.4, pp.3--9 (1985) Proceedings: Pires E., Machado J. and Oliveira P. "An Evolutionary Approach to Robot Structure and Trajectory Optimization", 10th International Conference on Advanced Robotics, pg. 333-338, Budapest, Hungary, August (2001) Journal: Sobh, T., Dekhil, M., Henderson T., and Sabbavarapu A. “Prototyping a Three Link Robot Manipulator”, International Journal of Robotics and Automation, Vol. 14, No. 2 (1999) Report: Dekhil, M., Sobh T., Henderson T., Sabbavarpu A. and Mecklenburg R. “Robot manipulator prototyping (Complete design review)”, University of Utah (1994) Books: Spong M. and Vidyasagar. “Robot Dynamics and Control”, Wiley, New York (1989) Images obtained from:

41. Thank You