mlsanityData to 7 Oct 2026

Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

arXiv 2302.07114 ↗First posted 14 Feb 2023cs.RO cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

5 of 27 references.

References · 5

  1. 2016FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planningcited by 34
  2. 2013Incremental Sampling-based Algorithm for Minimum-violation Motion Planningcited by 9
  3. 2021MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Spacecited by 7
  4. 2021Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspacescited by 7
  5. 2022Minimum Displacement Motion Planning for Movable Obstaclescited by 4

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Modelscited by 4
  2. 2025A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networkscited by 2
  3. 2025Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlapscited by 1
  4. 2025A Framework for Task and Motion Planning based on Expanding AND/OR Graphscited by 0