FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs

TL;DR

FiReFly introduces a distributed motion planning algorithm that ensures fair energy usage among multiple UAVs while maintaining mission success, demonstrating improved fairness and scalability in simulations.

Contribution

The paper presents a novel distributed fair motion planner, FiReFly, integrating fairness into multi-UAV planning and showing its effectiveness in simulations.

Findings

FiReFly produces fairer UAV trajectories.

It improves mission success rates over non-fair planners.

Real-time performance up to 15 UAVs, scalable to 50 with trade-offs.

Abstract

We propose injecting notions of fairness into multi-robot motion planning. When robots have competing interests, it is important to optimize for some kind of fairness in their usage of resources. In this work, we explore how the robots' energy expenditures might be fairly distributed among them, while maintaining mission success. We formulate a distributed fair motion planner and integrate it with safe controllers in a algorithm called FiReFly. For simulated reach-avoid missions, FiReFly produces fairer trajectories and improves mission success rates over a non-fair planner. We find that real-time performance is achievable up to 15 UAVs, and that scaling up to 50 UAVs is possible with trade-offs between runtime and fairness improvements.