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This research addresses the computational complexity of robot navigation by proposing efficient algorithms to displace movable obstacles when direct paths are blocked. By framing the problem as NP-hard and offering approximate solutions that balance path length with displacement effort, the work advances core machine learning and robotics planning methodologies.
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A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation

arXiv2 min read

arXiv:2609.22974v1 Announce Type: cross Abstract: In this paper, we investigate the Minimum Obstacle Displacement Planning problem from a robot motion planning perspective. The problem involves determining a feasible path to a goal location by displacing movable obstacles when no collision-free path initially exists. We show that this problem is computationally challenging and, in particular, NP-hard when obstacles are modeled as polygons in the plane. Besides an exact formulation of the minimum obstacle displacement problem generalizing other problems in the literature, and the associated optimal solution, this paper proposes an approximate solution that is less intensive from a computational standpoint, and differs from the optimal solution by a fraction of the optimal cost, being able to trade-off between path length and amount of obstacle displacements.

Abstract:In this paper, we investigate the Minimum Obstacle Displacement Planning problem from a robot motion planning perspective. The problem involves determining a feasible path to a goal location by displacing movable obstacles when no collision-free path initially exists. We show that this problem is computationally challenging and, in particular, NP-hard when obstacles are modeled as polygons in the plane. Besides an exact formulation of the minimum obstacle displacement problem generalizing other problems in the literature, and the associated optimal solution, this paper proposes an approximate solution that is less intensive from a computational standpoint, and differs from the optimal solution by a fraction of the optimal cost, being able to trade-off between path length and amount of obstacle displacements.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.22974 [cs.RO]

(or arXiv:2609.22974v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.22974 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 215 words

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