ROAIApr 27, 2022

Minimum Displacement Motion Planning for Movable Obstacles

arXiv:2204.12740v16 citationsh-index: 29
Originality Synthesis-oriented
AI Analysis

This addresses motion planning challenges in cluttered environments where obstacles can be moved, but it appears incremental as it builds on existing planning methods.

The paper tackles the problem of motion planning with movable obstacles by minimizing their displacement to find a feasible path, using a metric to penalize robot-obstacle intersections and iterative displacement, with examples demonstrating success.

This paper presents a minimum displacement motion planning problem wherein obstacles are displaced by a minimum amount to find a feasible path. We define a metric for robot-obstacle intersection that measures the extent of the intersection and use this to penalize robot-obstacle overlaps. Employing the actual robot dynamics, the planner first finds a path through the obstacles that minimizes the robot-obstacle intersections. The metric is then used to iteratively displace the obstacles to achieve a feasible path. Several examples are provided that successfully demonstrates the proposed problem.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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