Abstract
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.
Abstract (translated)
自主无人机竞速推动了高速运动规划和多智能体策略决策的边界。在这个领域取得成功需要无人机不仅要在极限条件下导航，还要能够预测并应对竞争对手的动作。本文研究了一个在该领域中出现的基本问题：一个智能体在采取行动之前应深入思考到什么程度？为此，我们比较了两种规划范式：模型预测博弈（Model Predictive Game, MPG），它以更长的计算时间为代价找到考虑到互动策略的方法；以及轮廓模型预测控制（contouring Model Predictive Control, MPC），这种方法可以快速制定策略但不考虑交互。通过广泛的实验来研究这一权衡，我们发现MPG在中等速度下优于MPC，但在高速时由于延迟问题而失去优势。为解决这个问题，我们提出了一种学习模型预测博弈（Learned Model Predictive Game, LMPG）方法，该方法将模型预测游戏进行分摊处理以减少延迟。通过模拟和硬件实验，在一对一竞速中将我们的方法与MPG和MPC进行了基准测试，发现LMPG优于两个基线方法。 这段翻译阐明了自主无人机竞速中的策略制定问题，并比较了几种不同的规划方法的效果，突出了所提出的LMPG方法在减少延迟的同时维持性能方面的优势。
URL
https://arxiv.org/abs/2602.06925
