Abstract
This paper addresses a smoother fixed-time obstacle-avoidance trajectory planning based on double-stranded ribonucleic acid (dsRNA) splicing evolutionary algorithm for a dual-arm free-floating space robot, the smoothness of large joint angular velocity is improved by 15.61% on average compared with the current trajectory planning strategy based on pose feedback, and the convergence performance is improved by 76.44% compared with the existing optimal trajectory planning strategy without pose feedback. Firstly, according to the idea of pose feedback, a novel trajectory planning strategy with low joint angular velocity input is proposed to make the pose errors of the end-effector and base converge asymptotically within fixed time. Secondly, a novel evolutionary algorithm based on the gene splicing idea of dsRNA virus is proposed to optimize the parameter of the fixed-time error response function and obstacle-avoidance algorithm, which can make joint angular velocity trajectory is planned smooth. In the end, the optimized trajectory planning strategy is applied into the dual-arm space robot system so that the robotic arm can smoothly, fast and accurately complete the tracking task. The proposed novel algorithm achieved 7.56–30.40% comprehensive performance improvement over the benchmark methods, experiment and simulation verify the effectiveness of the proposed method.
Highlights
With the development of robotic technology, robots have been widely used as human auxiliaries in space, factories, ocean and cities [1,2,3,4,5]
Yan et al [16] presented a fast obstacle-avoidance trajectory planning strategy by fixed-time stability to make the errors caused by the auxiliary algorithm converge asymptotically, but the current pose-feedback trajectory planning strategies lead to excessive joint angular velocity when the robotic arms need to avoid obstacles, which may accelerate the aging of the mechanical rotating parts
This paper presents a novel double-stranded ribonucleic acid (dsRNA) splicing evolutionary algorithm and a novel smooth, fixed-time and high-precision obstacle-avoidance trajectory planning method for a dual-arm free-floating space robot, and the proposed novel evolutionary algorithm achieved 7.56–30.40% performance improvement over benchmark methods
Summary
With the development of robotic technology, robots have been widely used as human auxiliaries in space, factories, ocean and cities [1,2,3,4,5]. Yan et al [16] presented a fast obstacle-avoidance trajectory planning strategy by fixed-time stability to make the errors caused by the auxiliary algorithm converge asymptotically, but the current pose-feedback trajectory planning strategies lead to excessive joint angular velocity when the robotic arms need to avoid obstacles, which may accelerate the aging of the mechanical rotating parts. The current trajectory optimization strategies based on evolutionary algorithm are often conservative and the results often fall into the local optimal, so it is important for the IGA to find the solution closest to the global optimal Motivated by these issues, this paper proposes a novel trajectory planning strategy for a dual-arm 6-DOF space robot with the following innovative points: (1). The smooth joint angular velocity trajectory of the space robot arm is planned by the dsRNA splicing evolutionary algorithm-based fixed-time trajectory planning strategy to make tracking errors of end-effector converge asymptotically in fixed time.
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