Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms

TL;DR

This paper presents a hybrid classical-quantum approach for optimizing robot trajectory planning at industrial scales, combining advanced algorithms and quantum hardware to improve solution quality and efficiency.

Contribution

It introduces a novel hybrid pipeline integrating random-key algorithms, ensemble techniques, and quantum annealing for scalable robot trajectory optimization.

Findings

Classical methods outperform greedy baselines.

Quantum annealing provides quantum-ready solutions.

Hybrid approach achieves industry-scale problem solutions.

Abstract

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorithm. Through a distinct separation of problem-independent and problem-dependent modules, we achieve an efficient problem representation, with a native encoding of constraints. We show that generalizations to alternative algorithmic paradigms such as simulated annealing are straightforward. We provide numerical benchmark results for industry-scale data sets. Our approach is found to consistently outperform greedy baseline results. To assess the capabilities of today's quantum hardware, we complement the classical approach with results obtained on quantum annealing hardware, using qbsolv on Amazon Braket. Finally, we show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.