Genetic Algorithms based method for time optimization in robotized site

Industrial implementation of robots is to perform the assigned tasks in the minimum possible time in the cycle comes up to increase productivity and reduce the cost. The cycle time is strongly linked to the robot trajectory cycle to the task. However, the optimization of the robot trajectory cycle the robot visited a set of points which represent the robotics task. Similar to persons in traveling the robot execute the task into shorter time if has a shorter path. However the trajectory cycle of the robot is strongly related to the displacement in coordinate space rather than operational space. In fact, the shorter distance between two task points is the shorter distance between two configurations. Since robot has different configurations in each task point the minimum trajectory should be chosen between each successive configuration. However the order of visiting the task point also affects the trajectory distance. Moreover the relative robot position to the task also has a trivial effect on the task time. In this work we develop a method to optimize the order of visiting the task point taking into consideration the robot configuration and the placement of the robot in the robotized site. Mainly, this method is based on Genetic Algorithms and it takes into consideration the multiplicity solutions of the robot Inverse Kinematics Model (IKM), the task point visit order and the placement of robot at the same time.

[1]  Steven Dubowsky,et al.  Planning time-optimal robotic manipulator motions and work places for point-to-point tasks , 1989, IEEE Trans. Robotics Autom..

[2]  Layek Abdel-Malek,et al.  The application of inverse kinematics in the optimum sequencing of robot tasks , 1990 .

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

[4]  Yifan Chen,et al.  CAD‐based automated robot trajectory planning for spray painting of free‐form surfaces , 2002 .

[5]  S. Dubowsky,et al.  Time optimal robotic manipulator motions and work places for point to point tasks , 1985, 1985 24th IEEE Conference on Decision and Control.

[6]  Liangsheng Qu,et al.  A Synergetic Approach to Genetic Algorithms for Solving Traveling Salesman Problem , 1999, Inf. Sci..

[7]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

[8]  Gamini Dissanayake,et al.  Workstation planning for redundant manipulators , 1994 .

[9]  Weihua Sheng,et al.  Robot Path Integration in Manufacturing Processes: Genetic Algorithm Versus Ant Colony Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

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

[11]  Yael Edan,et al.  Near-minimum-time task planning for fruit-picking robots , 1991, IEEE Trans. Robotics Autom..

[12]  Stefan Näher,et al.  The Travelling Salesman Problem , 2011, Algorithms Unplugged.

[13]  Padraig Cunningham,et al.  Using Case Retrieval to Seed Genetic Algorithms , 2001, Int. J. Comput. Intell. Appl..

[14]  Kang G. Shin,et al.  Selection of Near-Minimum Time Geometric Paths for Robotic Manipulators , 1985, 1985 American Control Conference.

[15]  Heung-Suk Hwang,et al.  An improved model for vehicle routing problem with time constraint based on genetic algorithm , 2002 .

[16]  A. Morgan,et al.  Solving the Kinematics of the Most General Six- and Five-Degree-of-Freedom Manipulators by Continuation Methods , 1985 .

[17]  Guangzhou Zeng,et al.  Study of genetic algorithm with reinforcement learning to solve the TSP , 2009, Expert Syst. Appl..