Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

TL;DR

This paper evaluates the MAPP algorithm for cooperative path finding of multiple UAVs in urban environments, demonstrating its efficiency in generating conflict-free trajectories for dozens of agents.

Contribution

It introduces and applies the decentralized MAPP algorithm to urban UAV navigation, highlighting its effectiveness compared to other approaches.

Findings

MAPP efficiently plans trajectories for dozens of UAVs.

Decentralized approach performs well in complex urban scenarios.

Experimental results confirm MAPP's suitability for real-time applications.

Abstract

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks.