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

@article{Schuetz2022OptimizationOR,
  title={Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms},
  author={Martin J. A. Schuetz and J. Kyle Brubaker and Henry Montagu and Yannick van Dijk and Johannes Klepsch and Philipp Ross and Andr{\'e} Luckow and Mauricio G. C. Resende and Helmut G. Katzgraber},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.03651}
}
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… 
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