TY - GEN
T1 - The Research of Intelligent Parallel Approach for Space Debris Grasping Manipulator Trajectory Planning
AU - Zhang, Jinyu
AU - Ning, Xin
AU - Bai, Muyan
AU - Ma, Shichao
N1 - Publisher Copyright:
©2024 by the International Astronautical Federation (IAF). All rights reserved.
PY - 2024
Y1 - 2024
N2 - As well known that an increasing number of end-of-life spacecraft occupy limited space orbits, generating a large amount of space debris, which poses a serious safety hazard to spacecraft launches and space activities. Therefore, active removal of space debris is an important prerequisite for ensuring the safety of space activities. Space manipulators are widely used in space missions due to their advantages of good flexibility and strong operational capability, and trajectory planning at the end of the manipulator is key to completing space debris removal missions. Considering the complexity of the space environment and the mission requirements, the autonomy and intelligence requirements of space manipulator are gradually increasing, and the currently commonly used planning methods are difficult to meet the increasingly complex mission requirements. In response to this challenge, this paper presents a multi-strategy parallel genetic algorithm (MSPGA). MSPGA improves population initialization by employing improved chaotic mapping, which improves the quality of the population. It also improves population diversity by introducing reverse population. In the individual selection process, MSPGA employs a binary bidding race strategy to retain the best individuals and reduce the risk of local optimization. In addition, MSPGA employs a multi-variant strategy to reduce the risk of relying on a single method, and this multi-method parallel strategy prevents the degradation of search capability caused by a single variant. The method is applied to the simulation of space debris grasping, the simulation results demonstrate the superior planning accuracy and efficiency of MSPGA compared to several commonly used algorithms. In particular, MSPGA demonstrates the ability to generate safe, feasible trajectories at reduced cost and faster speeds, thus confirming the validity and feasibility of its application in trajectory planning for space debris grasping by space robot arms.
AB - As well known that an increasing number of end-of-life spacecraft occupy limited space orbits, generating a large amount of space debris, which poses a serious safety hazard to spacecraft launches and space activities. Therefore, active removal of space debris is an important prerequisite for ensuring the safety of space activities. Space manipulators are widely used in space missions due to their advantages of good flexibility and strong operational capability, and trajectory planning at the end of the manipulator is key to completing space debris removal missions. Considering the complexity of the space environment and the mission requirements, the autonomy and intelligence requirements of space manipulator are gradually increasing, and the currently commonly used planning methods are difficult to meet the increasingly complex mission requirements. In response to this challenge, this paper presents a multi-strategy parallel genetic algorithm (MSPGA). MSPGA improves population initialization by employing improved chaotic mapping, which improves the quality of the population. It also improves population diversity by introducing reverse population. In the individual selection process, MSPGA employs a binary bidding race strategy to retain the best individuals and reduce the risk of local optimization. In addition, MSPGA employs a multi-variant strategy to reduce the risk of relying on a single method, and this multi-method parallel strategy prevents the degradation of search capability caused by a single variant. The method is applied to the simulation of space debris grasping, the simulation results demonstrate the superior planning accuracy and efficiency of MSPGA compared to several commonly used algorithms. In particular, MSPGA demonstrates the ability to generate safe, feasible trajectories at reduced cost and faster speeds, thus confirming the validity and feasibility of its application in trajectory planning for space debris grasping by space robot arms.
KW - Genetic algorithm
KW - Parallel mechanism
KW - Space debris
KW - Space manipulator
KW - Trajectory planning
UR - http://www.scopus.com/inward/record.url?scp=85219203706&partnerID=8YFLogxK
U2 - 10.52202/078360-0180
DO - 10.52202/078360-0180
M3 - 会议稿件
AN - SCOPUS:85219203706
T3 - Proceedings of the International Astronautical Congress, IAC
SP - 1873
EP - 1880
BT - 22nd IAA Symposium on Space Debris - Held at the 75th International Astronautical Congress, IAC 2024
PB - International Astronautical Federation, IAF
T2 - 22nd IAA Symposium on Space Debris at the 75th International Astronautical Congress, IAC 2024
Y2 - 14 October 2024 through 18 October 2024
ER -