Enhanced Decentralized Autonomous Aerial Robot Teams with Group Planning

TL;DR

This paper introduces an improved decentralized aerial robot team system that dynamically groups agents for better planning quality using group planning, demonstrated through simulations and real-world tests.

Contribution

It presents a novel group planning mechanism combining multi-agent pathfinding and trajectory optimization for decentralized aerial robot teams.

Findings

Enhanced planning success rate

Applicable to large-scale teams

Improved planning quality in experiments

Abstract

Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works in this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In this paper, we propose an enhanced decentralized autonomous aerial robot team system with group planning. According to the spatial distribution of agents, the system dynamically divides the team into several groups and isolated agents. For conflicts within each group, we propose a novel coordination mechanism named group planning. The group planning consists of efficient multi-agent pathfinding (MAPF) and trajectory joint optimization, which can significantly improve planning quality and success rate. We demonstrate through simulations and real-world experiments that our method not only has applicability for a large-scale team but also has top-level planning quality