Optimal trajectory optimization of 7R robot for space maintenance operation

Abstract

This paper studies the optimal trajectory optimization of a space robot for maintenance operations. A new trajectory planning algorithm is proposed to solve the problems of low maintenance efficiency and poor stationarity of space robots on-orbit. Firstly, considering the constraint conditions such as the speed, acceleration and secondary acceleration of each joint of the space robots, the cubic spline curve is used to plan the trajectory of the space robots in the joint space. Then, a comprehensive optimal planning model for time-jerk is established, and the optimal problem of space maintenance efficiency and stationarity is described as a nonlinear constrained optimization problem. Finally, a hybrid optimization algorithm is proposed to solve the trajectory optimization problem of space robots by combining genetic algorithm and particle swarm optimization method. The simulation results show that, compared with several classic optimization algorithms, the proposed hybrid optimization algorithm can obtain a better maintenance trajectory of the space robots under the constraints, which solves the low efficiency and poor stability of the space robots on-orbit maintenance problem.

Analysis and Verification on the Equivalence Between Jerk-Level and Acceleration-Level Schemes Applied to Manipulators Controlling

“…Then, according to the above analysis, it is concluded readily that theoretical solutions of acceleration-level scheme (4) and jerk-level scheme (6) are equivalent. Besides, the solution of jerk-level scheme (4) is able to converge exponentially to the theoretical solution of acceleration-level scheme (6). The parameter µ controls the exponential convergence speed, and a larger µ means a faster convergence.…”

“…After introducing two different-level schemes mentioned in the previous section, we exploit a neural-network-based algorithm to address the two schemes. Besides, on the basis of the algorithm, an analysis is conducted on the intrinsic consistency of acceleration-level scheme (4) and jerk-level scheme (6).…”

“…That is to say, to obtain the optimal solution of schemes (4) and (6), it is pre-requisite to find the solution of linear system (10). For solving linear system (10), a GNN algorithm is proposed and constructed as follows.…”

“…Compared with the matrix-inverse-based algorithm, GNN algorithm (13) is more suitable for dealing with the online control of manipulators. For problems (4) and (6), if matrix J is full rank, the two methods usually obtain the same solution. When the matrix is not full of rank, the result via computing the pseudoinverse may be large, i.e.,θ could be large, thus leading to a discontinuity phenomenon in joint velocities.…”

“…4(a), (b), and (c), respectively. It needs to be supplemented that blue lines represent the transient behaviors of the joint data generated by acceleration-level scheme (4), and the tiny red squares stand for the transient behaviors of the joint data generated by jerk-level scheme (6) in Fig. 4.…”