Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

TL;DR

This paper investigates approximate algorithms for the minimum obstacle displacement planning problem in robot navigation, aiming to reduce computational complexity while maintaining near-optimal path costs.

Contribution

It introduces approximate solutions that are less computationally intensive and provide bounded suboptimality compared to the optimal displacement planning.

Findings

Proposed algorithms significantly reduce computation time.

Achieved solutions are within a known factor of the optimal cost.

Demonstrated effectiveness in complex navigation scenarios.

Abstract

In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grows exponentially in the size of number of movable obstacles. This work looks into approximate solutions that are computationally less intensive and differ from the optimal solution by a factor of the optimal cost.