让无人机在竞速中快速决策，兼顾策略与速度
Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing
- 用学习方法优化预测博弈模型，降低计算延迟
- 实测显示新方法在仿真和真实飞行中均胜过传统方案
- 适合需要高速协同决策的多智能体系统研究
自主无人机竞速挑战着高速运动规划与多智能体策略决策的极限。成功不仅依赖于极限速度下的导航能力，还需预判并应对对手动作。本文探讨一个核心问题：智能体在行动前应进行多深的策略推演？我们对比了两种规划范式：模型预测博弈（MPG）能考虑交互但计算耗时，而轮廓模型预测控制（MPC）速度快但忽略交互。大量实验表明，MPG在中等速度下表现更优，但在高速下因延迟劣势明显。为此，我们提出学习型模型预测博弈（LMPG），通过学习将模型预测博弈开销分摊，显著降低延迟。在仿真与硬件实验中，头对头竞速测试显示，LMPG优于MPG和MPC。
原文摘要 · Abstract (English)
Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate at their limits but also to anticipate and counteract competitors' actions. In this paper, we study a fundamental question that arises in this domain: how deeply should an agent strategize before taking an action? To this end, we compare two planning paradigms: the Model Predictive Game (MPG), which finds interaction-aware strategies at the expense of longer computation times, and contouring Model Predictive Control (MPC), which computes strategies rapidly but does not reason about interactions. We perform extensive experiments to study this trade-off, revealing that MPG outperforms MPC at moderate velocities but loses its advantage at higher speeds due to latency. To address this shortcoming, we propose a Learned Model Predictive Game (LMPG) approach that amortizes model predictive gameplay to reduce latency. In both simulation and hardware experiments, we benchmark our approach against MPG and MPC in head-to-head races, finding that LMPG outperforms both baselines.
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