Efficient Multi-Objective Planning with Weighted Maximization Using Large Neighbourhood Search

TL;DR

This paper introduces a Large Neighbourhood Search algorithm for multi-objective planning that efficiently finds Pareto-optimal solutions, significantly reducing computation time compared to existing methods.

Contribution

The paper presents a novel Large Neighbourhood Search-based algorithm for weighted maximum planning, enabling faster computation of Pareto-optimal solutions in autonomous navigation.

Findings

Achieves solution quality comparable to existing methods.

Reduces runtime by 10 to 100 times.

Effectively finds solutions in non-convex trade-off spaces.

Abstract

Autonomous navigation often requires the simultaneous optimization of multiple objectives. The most common approach scalarizes these into a single cost function using a weighted sum, but this method is unable to find all possible trade-offs and can therefore miss critical solutions. An alternative, the weighted maximum of objectives, can find all Pareto-optimal solutions, including those in non-convex regions of the trade-off space that weighted sum methods cannot find. However, the increased computational complexity of finding weighted maximum solutions in the discrete domain has limited its practical use. To address this challenge, we propose a novel search algorithm based on the Large Neighbourhood Search framework that efficiently solves the weighted maximum planning problem. Through extensive simulations, we demonstrate that our algorithm achieves comparable solution quality to existing weighted maximum planners with a runtime improvement of 1-2 orders of magnitude, making it a viable option for autonomous navigation.