用深度学习提升太空机械臂避障规划精度与实时性
DK-RRT: Deep Koopman RRT for Collision-Aware Motion Planning of Space Manipulators in Dynamic Debris Environments
- 结合深度网络与柯普曼算子，学习碎片动态的非线性特征
- 实测显示规划效率比传统方法快，且抗干扰能力更强
- 适合需要自主避障的太空机器人任务，如在轨维修
在动态轨道碎片环境中，机械臂轨迹规划面临障碍物运动复杂和不确定性难题。本文提出深度柯普曼RRT（DK-RRT）框架，融合深度学习、柯普曼算子理论与快速探索随机树（RRT）。通过深度神经网络识别碎片动态的高效非线性嵌入，提升柯普曼模型预测精度，实现高实时性主动规划。利用在线传感器反馈持续优化预测模型，有效应对动态障碍物变化。仿真结果表明，相比传统RRT与常规柯普曼方法，DK-RRT在适应性、鲁棒性和计算效率方面均表现更优，展现出在自主空间操作任务中的应用潜力。
原文摘要 · Abstract (English)
Trajectory planning for robotic manipulators operating in dynamic orbital debris environments poses significant challenges due to complex obstacle movements and uncertainties. This paper presents Deep Koopman RRT (DK-RRT), an advanced collision-aware motion planning framework integrating deep learning with Koopman operator theory and Rapidly-exploring Random Trees (RRT). DK-RRT leverages deep neural networks to identify efficient nonlinear embeddings of debris dynamics, enhancing Koopman-based predictions and enabling accurate, proactive planning in real-time. By continuously refining predictive models through online sensor feedback, DK-RRT effectively navigates the manipulator through evolving obstacle fields. Simulation studies demonstrate DK-RRT's superior performance in terms of adaptability, robustness, and computational efficiency compared to traditional RRT and conventional Koopman-based planning, highlighting its potential for autonomous space manipulation tasks.
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