Motion planning for multi-link robots with artificial potential fields and modified simulated annealing
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In this thesis we present a hybrid control methodology using Artificial Potential Fields (APF) integrated with a modified Simulated Annealing (SA) optimization algorithm for motion planning of a multi-link robots team. The principle of this work is based on the locomotion of a snake where subsequent links follow the trace of the head. The proposed algorithm uses the APF method which provides simple, efficient and effective path planning and the modified SA is applied in order for the robots to recover from a local minima. Modifications to the SA algorithm improve the performance of the algorithm and reduce convergence time. Validation on a three-link snake robot shows that the derived control laws from the motion planning algorithm that combine APF and SA can successfully navigate the robot to reach its destination, while avoiding collisions with multiple obstacles and other robots in its path as well as recover from local minima. To improve the performance of the algorithm, the gradient descent method is replaced by Newton’s method which helps in reducing the zigzagging phenomenon in gradient descent method while the robot moves in the vicinity of an obstacle.