Benchmarking global optimization techniques for unmanned aerial vehicle path planning

TL;DR

This paper benchmarks various global optimization techniques on UAV path planning problems, revealing evolutionary algorithms as top performers and highlighting the need for further investigation into problem dimensionality effects.

Contribution

It introduces a new UAV path planning benchmark suite and compares 12 global optimization methods, providing insights into their performance across problem dimensions.

Findings

Evolutionary algorithms outperform other methods in UAV path planning.

Benchmark suite reveals unique characteristics of UAV optimization problems.

Performance varies with problem dimensionality and computational budget.

Abstract

The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select twelve well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated.