A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections
This addresses safe mobility in roads, factories, and warehouses, but appears incremental as it builds on existing RRT and evolutionary methods.
The paper tackled the problem of computationally expensive multi-robot motion planning at intersections by proposing a hybrid evolutionary algorithm combining parametric lattice-based configuration and discrete-based RRT, showing feasibility and superiority over seven other approaches in complex scenarios.
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.