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

Abstract 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Evolutionary multi-objective trajectory optimization for a redundant robot in Cartesian space considering obstacle avoidance ; volume:13 ; number:1 ; year:2022 ; pages:41-53 ; extent:13 ; Mechanical sciences ; 13, Heft 1 (2022), 41-53 (gesamt 13)

Classification and Topics

• Classification: Elektrotechnik, Elektronik

Contributors, Places and Time

• Creator: Liu, Yong ; Li, Xiang ; Jiang, Peiyang ; Du, Zhe ; Wu, Zhe ; Sun, Boxi ; Huang, Xinyan

Further information

• DOI: 10.5194/ms-13-41-2022
• URN: urn:nbn:de:101:1-2022021004174846416708
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST