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

Schuetz, Martin J. A. and Brubaker, J. Kyle and Montagu, Henry and van Dijk, Yannick and Klepsch, Johannes and Ross, Philipp and Luckow, Andre and Resende, Mauricio G. C. and Katzgraber, Helmut G. (2022) Optimization of Robot-Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms. Physical Review Applied, 18 (5). Art. No. 054045. ISSN 2331-7019. doi:10.1103/physrevapplied.18.054045.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Schuetz, Martin J. A.0000-0001-5948-6859
Brubaker, J. Kyle0000-0002-6439-5270
Ross, Philipp0000-0002-4720-9835
Luckow, Andre0000-0002-1225-4062
Katzgraber, Helmut G.0000-0003-3341-9943
Additional Information:We would like to thank Alexander Opfolter and Cory Thigpen for management of this collaboration between AWS and BMW. The AWS team thanks Shantu Roy for his invaluable guidance and support.
Group:AWS Center for Quantum Computing
Issue or Number:5
Record Number:CaltechAUTHORS:20221209-478595000.3
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118280
Deposited By: Research Services Depository
Deposited On:11 Jan 2023 15:14
Last Modified:11 Jan 2023 15:14

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