Optimal Trajectory Planning for Minimizing Base Disturbance of a Redundant Space Robot with IQPSO

With the development of aerospace technology, the practical application of a free-floating redundant space robot has become more and more popular. The problem of minimizing base disturbance has been paid attention among academic researchers. If the space robot moves, it would have an impact on the p...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2022-07, Vol.2022, p.1-12
Main Authors: Zhang, Jianxia, Yang, Fuchao, Nie, Guanghua, Zhang, Jianxin
Format: Article
Language:English
Subjects:
Algorithms
Fitness
Kinematic equations
Kinematics
Optimization
Particle swarm optimization
Polynomials
Redundancy
Robots
Robustness (mathematics)
Simulated annealing
Space robots
Trajectory optimization
Trajectory planning
Velocity
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Summary:With the development of aerospace technology, the practical application of a free-floating redundant space robot has become more and more popular. The problem of minimizing base disturbance has been paid attention among academic researchers. If the space robot moves, it would have an impact on the pose of a base. The interference on a base should be reduced, which was caused by the movements of the space robot. In the paper, the simplified model of a redundant space robot has been described, which consists of a base and a 7-joint manipulator. Using the nonholonomic redundancy features, the pose of the base has been optimized planning. First, a set of kinematic equations of the redundant space robot was founded. Second, the 5-order polynomial function could be used for the parametric 7 joints. Third, on the basis of the pose requirements, a fitness function was defined. At last, the proposed improved quantum particle swarm optimization (IQPSO) algorithm was presented. The proposed IQPSO algorithm not only searched the optimal value easily but also had a good robust performance. The advantages could be shown through the numerical experiments, compared with the quantum-behaved particle swarm optimization (QPSO) algorithm, particle swarm optimization (PSO) algorithm, and simulated annealing particle swarm (SAPSO) algorithm. Then, the proposed IQPSO algorithm was used to optimize the fitness function of trajectory planning. By the simulation results, it could be confirmed that the proposed IQPSO algorithm searched the global optimal solution not only easily but also smoothly, compared with the QPSO, PSO, and SAPSO algorithms. The proposed approach was suitable for planning an optimal trajectory.
ISSN:2090-0147
2090-0155
DOI:10.1155/2022/3398810