Evolutionary multi-objective trajectory optimization for a redundant robot in Cartesian space considering obstacle avoidance

A method of end-effector trajectory planning in Cartesian space based on multi-objective optimization is proposed in this paper to solve the collision problem during the motion of the redundant manipulator. First, a cosine polynomial function is used to interpolate the trajectory of the end effector...

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Veröffentlicht in:Mechanical Sciences Jg. 13; H. 1; S. 41 - 53
Hauptverfasser: Liu, Yong, Li, Xiang, Jiang, Peiyang, Du, Zhe, Wu, Zhe, Sun, Boxi, Huang, Xinyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Gottingen Copernicus GmbH 08.02.2022
Copernicus Publications
Schlagworte:
Algorithms
Analysis
Cartesian coordinates
End effectors
Energy consumption
Genetic algorithms
Inverse kinematics
Kinematics
Manipulators
Materials of engineering and construction. Mechanics of materials
Mathematical optimization
Multiple objective analysis
Obstacle avoidance
Optimization algorithms
Particle swarm optimization
Polynomials
Researchers
Robotics
Robots
TA401-492
Trajectory optimization
Trajectory planning
Trigonometric functions
Velocity
ISSN:2191-916X, 2191-9151, 2191-916X
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Zusammenfassung:A method of end-effector trajectory planning in Cartesian space based on multi-objective optimization is proposed in this paper to solve the collision problem during the motion of the redundant manipulator. First, a cosine polynomial function is used to interpolate the trajectory of the end effector, enabling it to reach the desired pose at a specific time. Then, the joint trajectory of the manipulator is solved by inverse kinematics, and the null space term is introduced as the joint limit constraint in the inverse kinematics equation. During the operation of the manipulator, the collision detection algorithm is employed to calculate the distance between the obstacle and each arm in real time. Finally, a multi-objective, multi-optimization model of trajectory that considers the obstacle avoidance, joint velocity, joint jerk and energy consumption is established and optimized with a multi-objective particle swarm optimization algorithm. The simulation results demonstrate that the proposed method can effectively accomplish the trajectory planning task and avoid obstacles; the joint trajectories obtained are smooth and meet the limit constraints; the joint jerk and energy consumption are well suppressed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2191-916X
2191-9151
2191-916X
DOI:10.5194/ms-13-41-2022