Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. In this paper, we introduce a novel approach called rotational obstacle avoidance method (ROAM) for adapting the initial dynamics when the workspace is partially occluded by obstacles. ROAM presents a closed-form solution that effectively avoids star-shaped obstacles in spaces of arbitrary dimensions by rotating the initial dynamics towards the tangent space. The algorithm enables navigation within obstacle hulls and can be customized to actively move away from surfaces, while guaranteeing the presence of only a single saddle point on the boundary of each obstacle. We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. Moreover, ROAM extends its capabilities to handle multi-obstacle environments and provides the ability to constrain dynamics within a safe tube. By utilizing weighted vector-tree summation, we successfully navigate around general concave obstacles represented as a tree-of-stars. Through experimental evaluation, ROAM demonstrates superior performance in terms of minimizing occurrences of local minima and maintaining similarity to the initial dynamics, outperforming existing approaches in multi-obstacle simulations. The proposed method is highly reactive, owing to its simplicity, and can be applied effectively in dynamic environments. This was demonstrated during the collision-free navigation of a 7 degree-of-freedom robot arm around dynamic obstacles

翻译：摘要：在机器人系统中控制复杂任务，例如清洁中的圆周运动或沿曲线行进，可通过非线性向量场来应对。本文提出一种名为旋转避障方法（ROAM）的新颖方法，用于在工作空间被障碍物部分遮挡时适配初始动力学。ROAM提供了一种闭式解，通过将初始动力学向切空间旋转，有效规避任意维度空间中的星形障碍物。该算法能够在障碍物内部导航，并可定制以主动远离表面，同时保证每个障碍物边界上仅存在单个鞍点。我们引入一系列映射将方法推广至一般非线性动力学。此外，ROAM将其能力扩展至多障碍物环境，并提供了将动力学约束于安全管道内的能力。通过利用加权向量树求和，我们成功绕过了以树状星形表示的通用凹形障碍物。实验评估表明，ROAM在最小化局部极小值出现频率和保持与初始动力学相似性方面表现出优越性能，在多障碍物仿真中优于现有方法。所提方法因其简洁性而具有高反应性，可有效应用于动态环境。这一点已在七自由度机械臂绕动态障碍物无碰撞导航的实验中得以验证。