# Object Placement on Cluttered Surfaces: A Nested Local Search Approach

## TL;DR

This paper presents a novel nested local search method for planning collision-free object placements on cluttered surfaces, optimizing for minimal displacements without prior goal configuration knowledge.

## Contribution

It introduces a multi-objective nested local search approach that efficiently computes object placements in cluttered environments without needing predefined goal states.

## Key findings

- High computational efficiency demonstrated in experiments.
- High success rate in finding feasible placements.
- Solutions with good quality and minimal object displacements.

## Abstract

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.