Avoidance of Concave Obstacles through Rotation of Nonlinear Dynamics

TL;DR

This paper introduces ROAM, a novel method for obstacle avoidance in robotic systems that rotates nonlinear dynamics to navigate around complex, concave, and multi-obstacle environments effectively and safely.

Contribution

The paper presents a closed-form rotational approach for obstacle avoidance that extends to multi-obstacle and dynamic environments, improving navigation performance and safety.

Findings

ROAM effectively avoids star-shaped and concave obstacles.

The method reduces local minima and maintains dynamics similarity.

ROAM outperforms existing approaches in multi-obstacle scenarios.

Abstract

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