Graph heuristics speed up robot motion planning with fewer obstacles removed
Graph-Based Heuristics for Minimum Constraint Removal in Cluttered Robot Motion Planning
Robotics
Summary
Robots moving around cluttered spaces sometimes can’t find a way through because many objects block their path. Solving this by removing the smallest number of obstacles to clear a path is hard and slow. The authors created a new method that uses graph-based shortcuts to quickly guess which obstacles to remove, trading off some accuracy for much faster planning. Their method matches exact solutions in some tests and stays close in others while cutting computation time significantly.
What this means in practice
- •For robotics engineers: Reduce computation time in cluttered environments by quickly identifying minimal obstacles to move for collision-free paths.
- •For automation system integrators: Improve robotic manipulation task setups by selecting fewer objects to reposition, speeding overall task completion.
Authors
Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto
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. The Minimum Constraint Removal problem, which we refer to as MCR, addresses this situation by identifying the minimum number of constraints, such as movable objects, whose removal enables a collision-free path. However, computing an optimal MCR solution is NP-hard. In this work, we propose a graph-based heuristic pipeline that combines bidirectional feasible-set generation with fixed- length searches over reduced MCR graphs. We further derive an extended feasible-set generator for instances in which the candidates produced by the first generator do not contain a complete optimal removal set. 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. 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. These results indicate that the proposed approach provides a practical compromise between computational efficiency and solution quality for motion planning among movable obstacles.