自然に着想を得たハイブリッド量子アルゴリズムによるロボット軌道計画の最適化
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
ランダムキー遺伝的アルゴリズムと量子アニーリングを組み合わせ、産業規模のロボット軌道計画を効率的に解くハイブリッド手法を提案し、従来の貪欲法を上回る性能を示した。
著者: Martin J. A. Schuetz, J. Kyle Brubaker, Henry Montagu, Yannick van Dijk, Johannes Klepsch, Philipp Ross, Andre Luckow, Mauricio G. C. Resende, Helmut G. Katzgraber
分類: quant-ph, cond-mat.dis-nn, cs.NE, cs.RO, math.OC
原文アブストラクト
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.