Effective motion planning strategy for space robot capturing targets under consideration of the berth position
Effective motion planning strategy for space robot capturing targets under consideration of the berth position
复制标题
考虑泊位位置的空间机器人捕获目标有效运动规划策略
DOI：
10.1016/j.actaastro.2018.04.029
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发表时间：
2018-07
影响因子：
3.5
通讯作者：
Liu Jinguo
中科院分区：
文献类型：
--
作者：
Zhang Xin;Liu Jinguo
虽然在文献中可以找到许多涉及空间机器人捕获浮动目标的任务的运动规划策略，但相对较少讨论如何选择航天器基地悬停的泊位位置。实际上，泊位位置是一个灵活可控的因素，选择合适的泊位位置对提高捕获使命中运动规划的效率有很大影响。因此，为了充分利用空间机器人的机动性，本文提出了一种新的观点，即利用基座位置作为一个可优化的参数来制定一个更全面、更有效的运动规划策略。考虑空间机器人的动力学耦合、动力学奇异性和物理限制，提出了一种基于正运动学和参数优化技术的统一运动规划框架，将规划问题转化为参数优化问题。为了摆脱捕获使命中严格的抓取位置约束，提出了抓取区域的概念，大大简化了运动规划的难度。并利用罚函数法构造了一个新的简洁的目标函数。这里，采用智能算法--粒子群优化算法（PSO）作为求解器来确定自由参数。两种捕获情况，即，捕获二维（2D）平面目标和捕获三维（3D）空间目标。相应的仿真结果表明，该方法是更有效的和有效的捕获任务规划。
Although many motion planning strategies for missions involving space robots capturing floating targets can be found in the literature, relatively little has discussed how to select the berth position where the spacecraft base hovers. In fact, the berth position is a flexible and controllable factor, and selecting a suitable berth position has a great impact on improving the efficiency of motion planning in the capture mission. Therefore, to make full use of the manoeuvrability of the space robot, this paper proposes a new viewpoint that utilizes the base berth position as an optimizable parameter to formulate a more comprehensive and effective motion planning strategy. Considering the dynamic coupling, the dynamic singularities, and the physical limitations of space robots, a unified motion planning framework based on the forward kinematics and parameter optimization technique is developed to convert the planning problem into the parameter optimization problem. For getting rid of the strict grasping position constraints in the capture mission, a new conception of grasping area is proposed to greatly simplify the difficulty of the motion planning. Furthermore, by utilizing the penalty function method, a new concise objective function is constructed. Here, the intelligent algorithm, Particle Swarm Optimization (PSO), is worked as solver to determine the free parameters. Two capturing cases, i.e., capturing a two-dimensional (2D) planar target and capturing a three-dimensional (3D) spatial target, are studied under this framework. The corresponding simulation results demonstrate that the proposed method is more efficient and effective for planning the capture missions.
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DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
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发表时间：
2007-02
期刊：
Robotics Auton. Syst.
影响因子：
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作者：
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通讯作者：
Ioannis Tortopidis;E. Papadopoulos
影响因子：
3.2
作者：
Panfeng Huang;Yangsheng Xu;Bin Liang
通讯作者：
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DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
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作者：
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通讯作者：
Federico Marini;Beata Walczak