Planning the motion path for a tightly coupled dual-arm space manipulator under closed-chain constraints is a fundamental yet challenging problem in on-orbit assembly of large-scale space structures. The closed-chain constraints significantly reduce the feasible configuration space, making it difficult for existing planners to efficiently generate collision-free motions, especially in cluttered environments. To address this issue, this paper proposes a task-space constrained bidirectional rapidly-exploring random tree algorithm, termed TCBiRRT. Unlike conventional methods that operate in the high-dimensional configuration space, the proposed approach performs random sampling and node expansion directly in the task space defined by the manipulated object pose. A task-space node expansion strategy is developed to generate candidate object motions, which are then mapped to continuous joint paths using a path inverse kinematics algorithm. The method is further integrated with a bidirectional RRT framework and a regrasp mechanism to efficiently connect two random trees. Extensive simulations are conducted in representative on-orbit assembly scenarios with varying levels of environmental complexity. The results demonstrate that TCBiRRT achieves significantly higher success rates and orders-of-magnitude improvements in planning time compared to state-of-the-art planners. The proposed method provides an efficient and robust solution for motion planning of tightly coupled dual-arm space manipulators.

翻译：在闭合链约束下规划紧耦合双机械臂空间机器人的运动路径，是大规模空间结构在轨装配中一个基础但具有挑战性的问题。闭合链约束显著降低了可行构型空间，使得现有规划器难以高效生成无碰撞运动，尤其在复杂环境中。针对这一问题，本文提出一种任务空间约束双向快速随机树算法，称为TCBiRRT。与传统方法在高维构型空间中运行不同，所提方法直接在被操作物体姿态定义的任务空间中进行随机采样和节点扩展。开发了一种任务空间节点扩展策略以生成候选物体运动，并通过路径逆运动学算法将其映射为连续关节路径。该方法进一步与双向RRT框架和重抓取机制集成，以实现两棵随机树的高效连接。在具有不同环境复杂度的代表性在轨装配场景中进行了大量仿真实验。结果表明，与现有最优规划器相比，TCBiRRT实现了显著更高的成功率以及数量级的规划时间提升。所提方法为紧耦合双机械臂空间机器人的运动规划提供了一种高效且鲁棒的解决方案。