Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning - Bureerat, Sujin; Pholdee, Nantiwat; Radpukdee, Thana; Jaroenapibal, Papot (Expert systems with applications, 2019)
•Efficient self-adaptive multiobjective meta-heuristic (MOMH) algorithm.•Use of success-history based parameter adaptation for optimisation parameters.•Comparative results of a robot path planning problem with well-established MOMHs.•New design results set as the baseline for further studies. This work presents self-adaptive multiobjective real-code population-based incremental learning hybridised with differential evolution (MRPBIL-DE) for solving a 6D robot trajectory planning multiobjective optimisation problem. The objective functions are assigned to minimise travelling time and minimise maximum jerk taking place during motion while the constraints are velocity, acceleration and jerk constraints. A five order polynomial function is used to represent a motion equation while the motion path is divided into two sub-paths; from initial to intermediate positions and from intermediate to final positions. The optimiser is used to find a set of design variables including joint positions, velocities and accelerations at intermediate positions, moving time from the initial to intermediate positions, and that from the intermediate to final positions. Several multiobjective meta-heuristics (MOMHs) along with the proposed algorithm are used to solve the trajectory optimisation problem of robot manipulators while their performances are investigated. The results indicated that the proposed algorithm is effective and efficient for multiobjective robot trajectory planning optimisation problem. The results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation.