Graph-Based Heuristics for Minimum Constraint Removal in Cluttered Robot Motion Planning

What happened

arXiv:2610.12609v1 Announce Type: new Abstract: Humanoid and service robots operating in clut- tered human environments may encounter motion planning problems that are infeasible when all surrounding objects are treated as fixed obstacles. Across six benchmarks, the evaluated pipeline matches the exact solution in three cases and, in the remaining cases, selects at most 31.25% more obstacles than the optimum, while being between 3.4 and 33.7 times faster than exact search.

These results indicate that the proposed approach provides a practical compromise between computational efficiency and solution quality for motion planning among movable obstacles. However, computing an optimal MCR solution is NP-hard. In one of the benchmarks, involving a manipulation task amidst clutter, selecting two rather than seven obstructing objects reduces the combined MCR, inverse- kinematics, and motion planning time from 84.72 s to 30.59 s.

Key facts

• These results indicate that the proposed approach — provides: a practical compromise between computational efficiency and solution quality for motion planning among movable obstacles