Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Robot Motion Planning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
Planning Tours of Robotic Arms among Partitioned Goals
International Journal of Robotics Research
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
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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.