Trajectory Optimization Methods of a Space Hyper-redundant Robot Based on Effective Arm-Shape Measurement

Space hyper-redundant robots (SHRRs) will play an increasingly important role in unstructured and complex environments due to their slender body and strong movement flexibility. However, the arm shape and end-effector pose of SHRRs have poor positioning accuracy, which influences the task execution effect. To solve the above problems, this article proposes a trajectory optimization method of SHRRs based on effective arm-shape measurement. First, aiming at the on-orbit service task in the narrow space (i.e., satellite maintenance), the arm-shape sensing framework based on the multicamera fusion (MCF) is developed. Then, the MCF-based pose optimization model of circular targets (i.e., circular cross sections of SHRRs) is established, where the observed results from different perspectives are fused. The particle swarm optimization (PSO) method is used to search for the optimal elliptic parameters. The centroid pose of circular cross sections and the configuration of end-constrained segments are reconstructed. Furthermore, based on the developed model, the optimized arm shape is used for the multiobjective trajectory optimization method. Finally, a simulation and experimental system for arm-shape sensing and trajectory optimization of SHRRs is built. The simulation and experimental results show that the proposed method have higher accuracy.