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

arXiv: https://arxiv.org/abs/2206.03651 · HTML: https://mlsanity.com/paper/2206.03651/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [A random-key GRASP for combinatorial optimization](https://mlsanity.com/paper/2405.18681/) (2024)
- [An Optimization Case Study for solving a Transport Robot Scheduling Problem on Quantum-Hybrid and Quantum-Inspired Hardware](https://mlsanity.com/paper/2309.09736/) (2023)
- [A Random-Key Optimizer for Combinatorial Optimization](https://mlsanity.com/paper/2411.04293/) (2024)
- [Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling](https://mlsanity.com/paper/2501.10243/) (2025)
- [Random-key genetic algorithms: Principles and applications](https://mlsanity.com/paper/2506.02120/) (2025)

## Abstract (arXiv)

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.