A Multi-Objective Genetic Algorithm-Based Optimal Trajectory Planning for 5-DoF Robotic Arm

The generation of optimal trajectories with multiple objectives is a common requirement in both mobile robots and industrial robots. The drawback of using conventional inverse kinematic solution methods in industrial robots is that they usually provide multiple solutions, and sometimes the problem is unsolvable. The motivation for carrying out this work is to use the heuristic method to find an optimum trajectory for multi-segment trajectory planning problem. Heuristic methods provide not only a satisfactory solution to the problem, but also less computational effort. The genetic algorithm is one such heuristic method used for this purpose. This chapter presents a genetic algorithm-based trajectory for a 5-DoF robot. The highlights of this work are the generation of an optimal path and trajectory with the objective of minimizing the sum of positional accuracy and execution time simultaneously. This work also compared the results of the combined effect with minimizing positional accuracy alone. It is observed that the combined effect of minimizing the time and positional error generates trajectories with minimum execution time compared to minimizing positional accuracy alone. The trajectories are interpolated with linear segment polynomial blend (LSPB). The generated optimal paths are simulated using MATLAB to check for singularities.