# Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

arXiv: https://arxiv.org/abs/2302.07114 · HTML: https://mlsanity.com/paper/2302.07114/

## Key facts

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

## Builds on (most cited first, 5 of 5)

- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [Incremental Sampling-based Algorithm for Minimum-violation Motion Planning](https://mlsanity.com/paper/1305.1102/) (2013)
- [MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space](https://mlsanity.com/paper/2104.04696/) (2021)
- [Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces](https://mlsanity.com/paper/2110.04089/) (2021)
- [Minimum Displacement Motion Planning for Movable Obstacles](https://mlsanity.com/paper/2204.12740/) (2022)

## Built on by (most cited first, 5 of 5)

- [IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models](https://mlsanity.com/paper/2503.10110/) (2025)
- [A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2503.07700/) (2025)
- [Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps](https://mlsanity.com/paper/2511.12203/) (2025)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [A Framework for Task and Motion Planning based on Expanding AND/OR Graphs](https://mlsanity.com/paper/2509.00317/) (2025)

## 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.