Task scheduling and motion planning for an industrial manipulator

In many robotic industrial applications, a manipulator should move among obstacles and reach a set of task-points in order to perform a pre-defined task. It is quite important as well as very complicated to determine the time-optimum sequence of the task-points visited by the end-effector's tip only once assuring that the manipulator's motion through the successive task-points is collision-free. This paper introduces a method for simultaneously planning collision-free motion and scheduling time-optimal route along a set of given task-points. This method is based on the projection of the workspace and the robot on the B-Surface to formulate an objective function for the minimization of the cycle time in visiting multiple task-points and taken into account the multiple solutions of the inverse kinematics and the obstacle avoidance. A modified GA with special encoding to encounter the multiplicity of the robot inverse kinematics and the required intermediate configurations is used for the searching of the optimal solution on the B-Surface. The simulation results show the efficiency and the effectiveness of the proposed approach to determine a suboptimal tour for multi-goal motion planning in complex environments cluttered with obstacles.

[1]  Eugene L. Lawler,et al.  Traveling Salesman Problem , 2016 .

[2]  ScienceDirect.  Robotics and computer-integrated manufacturing , 1984 .

[3]  Beatrice M. Ombuki-Berman,et al.  Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows , 2006, Applied Intelligence.

[4]  Tomás Lozano-Pérez,et al.  Spatial Planning: A Configuration Space Approach , 1983, IEEE Transactions on Computers.

[5]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[6]  Nikos A. Aspragathos,et al.  Obstacle representation by Bump-surfaces for optimal motion-planning , 2005, Robotics Auton. Syst..

[7]  John H. Reif,et al.  Complexity of the mover's problem and generalizations , 1979, 20th Annual Symposium on Foundations of Computer Science (sfcs 1979).

[8]  Aristides A. G. Requicha,et al.  Multiple-goals path planning for coordinate measuring machines , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Christian Wurll,et al.  Point-to-point and multi-goal path planning for industrial robots , 2001, J. Field Robotics.

[10]  Khelifa Baizid,et al.  Genetic Algorithms based method for time optimization in robotized site , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Heinz Wörn,et al.  MULTI-GOAL PATH PLANNING FOR INDUSTRIAL ROBOTS , 1999 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Jayantha Katupitiya,et al.  Mobile Manipulator Motion Planning Towards Multiple Goal Configurations , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Nikos A. Aspragathos,et al.  Optimal robot task scheduling based on genetic algorithms , 2005 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  Gregory Gutin,et al.  The traveling salesman problem , 2006, Discret. Optim..

[17]  Les A. Piegl,et al.  The NURBS Book , 1995, Monographs in Visual Communication.

[18]  G. Swaminathan.  Robot Motion Planning , 2006 .

[19]  Tim Roughgarden,et al.  Planning Tours of Robotic Arms among Partitioned Goals , 2006, Int. J. Robotics Res..