Creating Star Worlds: Reshaping the Robot Workspace for Online Motion Planning

TL;DR

This paper introduces a novel workspace reshaping algorithm that transforms intersecting obstacles into a disjoint star world, enhancing the applicability of reactive motion planning methods like harmonic potential fields in complex environments.

Contribution

The paper presents a new algorithm based on admissible kernel and starshaped hull concepts to reshape intersecting obstacles into disjoint star-shaped regions for improved motion planning.

Findings

Successfully reshapes intersecting obstacles into star-shaped regions

Enables reactive motion planning in more complex, real-world environments

Demonstrates effectiveness with 2D robot workspace examples

Abstract

Motion planning methods like navigation functions and harmonic potential fields provide (almost) global convergence and are suitable for obstacle avoidance in dynamically changing environments due to their reactive nature. A common assumption in the control design is that the robot operates in a disjoint star world, i.e. all obstacles are strictly starshaped and mutually disjoint. However, in real-life scenarios obstacles may intersect due to expanded obstacle regions corresponding to robot radius or safety margins. To broaden the applicability of aforementioned reactive motion planning methods, we propose a method to reshape a workspace of intersecting obstacles into a disjoint star world. The algorithm is based on two novel concepts presented here, namely admissible kernel and starshaped hull with specified kernel, which are closely related to the notion of starshaped hull. The utilization of the proposed method is illustrated with examples of a robot operating in a 2D workspace using a harmonic potential field approach in combination with the developed algorithm.