# A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections

arXiv: https://arxiv.org/abs/2412.01082 · HTML: https://mlsanity.com/paper/2412.01082/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning](https://mlsanity.com/paper/1903.00994/) (2019)
- [Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning](https://mlsanity.com/paper/1305.2889/) (2013)
- [db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning](https://mlsanity.com/paper/2309.16445/) (2023)
- [Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning](https://mlsanity.com/paper/2303.01619/) (2023)
- [Scheduling Autonomous Vehicle Platoons Through an Unregulated Intersection](https://mlsanity.com/paper/1609.04512/) (2016)
- [Multi-Robot Coordinated Planning in Confined Environments under Kinematic Constraints](https://mlsanity.com/paper/1910.03101/) (2019)

## Abstract (arXiv)

Coordinated multi-robot motion planning at intersections is key for safe mobility in roads, factories and warehouses. The rapidly exploring random tree (RRT) algorithms are popular in multi-robot motion planning. However, generating the graph configuration space and searching in the composite tensor configuration space is computationally expensive for large number of sample points. In this paper, we propose a new evolutionary-based algorithm using a parametric lattice-based configuration and the discrete-based RRT for collision-free multi-robot planning at intersections. Our computational experiments using complex planning intersection scenarios have shown the feasibility and the superiority of the proposed algorithm compared to seven other related approaches. Our results offer new sampling and representation mechanisms to render optimization-based approaches for multi-robot navigation.