让多架无人机公平分配能耗，提升任务成功率。
FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs
- 分布式规划算法动态平衡各无人机能耗
- 仿真中比非公平规划提升任务成功率达15%以上
- 支持15架实时运行，50架可扩展但需权衡性能
我们提出在多机器人运动规划中引入公平性概念。当机器人存在资源竞争时，合理分配能耗至关重要。本文设计了一种分布式公平运动规划方法，并与安全控制器结合，形成名为FiReFly的算法。在模拟的规避任务中，FiReFly生成更公平的轨迹，相比非公平规划显著提升任务成功率。实验表明，该算法在15架无人机下可实现实时性能；扩展至50架无人机虽可行，但需在运行时间和公平性提升间做权衡。
原文摘要 · Abstract (English)
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.
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