# Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps

arXiv: https://arxiv.org/abs/2511.12203 · HTML: https://mlsanity.com/paper/2511.12203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [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)
- [Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2302.07114/) (2023)
- [Minimum Displacement Motion Planning for Movable Obstacles](https://mlsanity.com/paper/2204.12740/) (2022)
- [Revisiting the Minimum Constraint Removal Problem in Mobile Robotics](https://mlsanity.com/paper/2305.01272/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2609.22974/) (2026)

## Abstract (arXiv)

We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. The first stage proceeds by computing a trajectory through the obstacles while minimizing an appropriate objective function. In the second stage, these obstacles are displaced to make the computed robot trajectory feasible, that is, collision-free. Several examples are provided that successfully demonstrate our approach on two distinct classes of constraint displacement problems.