Problem
Research questions and friction points this paper is trying to address.
multi-objective planning
weighted maximization
Pareto-optimal solutions
autonomous navigation
computational complexity
Efficient Multi-Objective Planning with Weighted Maximization Using Large Neighbourhood Search
🤖 AI Summary
This work addresses the challenge of efficiently exploring non-convex Pareto fronts in multi-objective autonomous navigation planning. While traditional weighted-sum approaches fail to capture such non-convex fronts, the Chebyshev scalarization (weighted max) method can recover the full Pareto front but suffers from high computational cost, limiting its practicality. To overcome this limitation, this study introduces Large Neighbourhood Search into the Chebyshev scalarization framework, proposing a novel algorithm for solving discrete multi-objective optimization problems. The proposed method achieves solution quality comparable to existing Chebyshev-based planners while reducing runtime by one to two orders of magnitude. This substantial improvement in computational efficiency enables comprehensive exploration of non-convex Pareto-optimal solutions in real-world autonomous navigation scenarios.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Weighted Maximum
Large Neighbourhood Search
Multi-Objective Planning
Pareto-Optimal Solutions
Autonomous Navigation
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