复杂环境中具备全身安全性的移动操作臂局部反应式控制
Local Reactive Control for Mobile Manipulators with Whole-Body Safety in Complex Environments
AI总结：
本文提出面向移动操作臂的空间域多步局部反应式控制器，利用AL-DDP处理解耦的连杆级避碰约束，显著提升狭窄杂乱环境中的安全性、效率与任务完成率。
摘要：
移动操作臂由于具有高维状态空间和复杂运动学，在狭窄、杂乱环境中导航时通常会面临重大挑战。尽管反应式方法在动态场景中表现出色，但它们难以高效地纳入整个状态空间中复杂且耦合的约束。在本工作中，我们提出一种新颖的局部反应式控制器，利用串联运动链的传播，将时域单步问题重构为空间域中的多步优化问题。这一转换便于制定定制化、解耦的特定连杆约束，并通过增广拉格朗日微分动态规划（AL-DDP）进一步高效求解。我们的方法在前向传播中自然吸收空间运动学传播，并在反向传播期间同时处理所有特定连杆约束，从而增强约束管理和计算效率。值得注意的是，在此框架中，我们利用提取的自由区域和精确几何模型为每个连杆制定避碰约束，这提高了移动操作臂在狭窄、杂乱空间中的机动性。实验结果表明，安全性、效率和任务完成率均有显著提升。这些发现强调了所提方法的鲁棒性，尤其是在传统方法可能失效的狭窄、杂乱环境中。开源项目可在 https://github.com/Chunx1nZHENG/MM-with-Whole-Body-Safety-Release.git 获取。
英文摘要：
Mobile manipulators typically encounter significant challenges in navigating narrow, cluttered environments due to their high-dimensional state spaces and complex kinematics. While reactive methods excel in dynamic settings, they struggle to efficiently incorporate complex, coupled constraints across the entire state space. In this work, we present a novel local reactive controller that reformulates the time-domain single-step problem into a multi-step optimization problem in the spatial domain, leveraging the propagation of a serial kinematic chain. This transformation facilitates the formulation of customized, decoupled link-specific constraints, which is further solved efficiently with augmented Lagrangian differential dynamic programming (AL-DDP). Our approach naturally absorbs spatial kinematic propagation in the forward pass and processes all link-specific constraints simultaneously during the backward pass, enhancing both constraint management and computational efficiency. Notably, in this framework, we formulate collision avoidance constraints for each link using accurate geometric models with extracted free regions, and this improves the maneuverability of the mobile manipulator in narrow, cluttered spaces. Experimental results showcase significant improvements in safety, efficiency, and task completion rates. These findings underscore the robustness of the proposed method, particularly in narrow, cluttered environments where conventional approaches could falter. The open-source project can be found at https://github.com/Chunx1nZHENG/MM-with-Whole-Body-Safety-Release.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
