Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning

•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 w...

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Published in:	Expert systems with applications Vol. 136; pp. 133 - 144
Main Authors:	Bureerat, Sujin, Pholdee, Nantiwat, Radpukdee, Thana, Jaroenapibal, Papot
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 01.12.2019 Elsevier BV
Subjects:	Acceleration Algorithms Equations of motion Evolutionary computation Mathematical analysis Multiobjective meta-heuristic algorithm Multiple objective analysis Optimisation Pneumatics Polynomials Robot arms Robot trajectory planning multiobjective Robots Self-adaptive algorithm Time-jerk minimisation Trajectory optimization Trajectory planning Travel time Time-jerk minimisation Multiobjective meta-heuristic algorithm Robot trajectory planning multiobjective Optimisation Self-adaptive algorithm
ISSN:	0957-4174, 1873-6793
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Abstract	•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.
AbstractList	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. •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.
Author	Jaroenapibal, Papot Bureerat, Sujin Pholdee, Nantiwat Radpukdee, Thana
Author_xml	– sequence: 1 givenname: Sujin orcidid: 0000-0002-6332-1202 surname: Bureerat fullname: Bureerat, Sujin email: sujbur@kku.ac.th organization: Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, 40002, Thailand – sequence: 2 givenname: Nantiwat orcidid: 0000-0002-4370-5241 surname: Pholdee fullname: Pholdee, Nantiwat email: nantiwat@kku.ac.th organization: Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, 40002, Thailand – sequence: 3 givenname: Thana surname: Radpukdee fullname: Radpukdee, Thana organization: Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, 40002, Thailand – sequence: 4 givenname: Papot surname: Jaroenapibal fullname: Jaroenapibal, Papot email: papoja@kku.ac.th organization: Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, 40002, Thailand
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Keywords	Time-jerk minimisation Multiobjective meta-heuristic algorithm Robot trajectory planning multiobjective Optimisation Self-adaptive algorithm
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Snippet	•Efficient self-adaptive multiobjective meta-heuristic (MOMH) algorithm.•Use of success-history based parameter adaptation for optimisation... This work presents self-adaptive multiobjective real-code population-based incremental learning hybridised with differential evolution (MRPBIL-DE) for solving...
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SubjectTerms	Acceleration Algorithms Equations of motion Evolutionary computation Mathematical analysis Multiobjective meta-heuristic algorithm Multiple objective analysis Optimisation Pneumatics Polynomials Robot arms Robot trajectory planning multiobjective Robots Self-adaptive algorithm Time-jerk minimisation Trajectory optimization Trajectory planning Travel time
Title	Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning
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