Object Placement on Cluttered Surfaces: A Nested Local Search Approach
This addresses a practical challenge in robotics and automation for real-world clutter scenarios, though it appears incremental as it builds on local search techniques.
The paper tackles the problem of placing objects on cluttered surfaces without prior goal configurations by introducing a method that computes collision-free placements while minimizing displacements, achieving high computational efficiency and success rates in experiments.
For planning rearrangements of objects in a clutter, it is required to know the goal configuration of the objects. However, in real life scenarios, this information is not available most of the time. We introduce a novel method that computes a collision-free placement of objects on a cluttered surface, while minimizing the total number and amount of displacements of the existing moveable objects. Our method applies nested local searches that perform multi-objective optimizations guided by heuristics. Experimental evaluations demonstrate high computational efficiency and success rate of our method, as well as good quality of solutions.