FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs

2025-08-19

AI Summary

In multi-UAV cooperative missions, resource competition leads to unfair energy consumption distribution. Method: This paper proposes a distributed model predictive control (MPC) framework incorporating fairness constraints, embedding fairness criteria—such as energy consumption variance minimization—into a distributed optimization architecture, integrated with safety-critical controllers and real-time re-planning to ensure collision avoidance and mission completion. Contributions/Results: (1) A provably convergent trade-off model balancing fairness and performance; (2) Scalable design supporting dynamic fleet expansion—validated in real-time experiments with 15 UAVs and simulated scalability up to 50 UAVs; (3) On standard reach-avoid tasks, the method significantly improves trajectory fairness (37% reduction in energy consumption standard deviation) and mission success rate (+12.6%) over baseline approaches.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensuring fair energy distribution among multiple UAVs
• Maintaining mission success during fair resource allocation
• Developing real-time fair motion planning for scalability

Innovation

• Fair distributed motion planning algorithm
• Integrates safe controllers with fairness
• Real-time performance for multiple UAVs