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A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
IEEE Transactions on Robotics ( IF 7.8 ) Pub Date : 2020-10-01 , DOI: 10.1109/tro.2020.2994881
Riccardo Spica , Eric Cristofalo , Zijian Wang , Eduardo Montijano , Mac Schwager

In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms a model predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others’ relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to ${1.25\,}\mathrm{{m}}/\mathrm{{s}}$.

中文翻译:

自动两人无人机赛车的实时博弈论规划器

在本文中,我们提出了一种在线 3-D 规划算法,用于无人机与单个敌方无人机进行竞争。该算法在每个时间步长计算两架无人机轨迹联合空间中纳什均衡的近似值,并以后退地平线方式进行。该算法在迭代最佳响应计算方案中使用一个新颖的灵敏度项来近似计算对手为了避免碰撞而向自我无人机屈服的量。这导致赛车轨迹比没有灵敏度术语更具竞争力。我们证明了这种灵敏度增强迭代最佳响应的不动点满足纳什均衡的一阶最优条件。我们展示了具有 2-D 和 3-D 赛道的比赛模拟研究的结果,表明我们的博弈论规划器明显优于模型预测控制 (MPC) 赛车算法。我们还在 3D 轨道上展示了多个无人机竞赛实验的结果,其中无人机通过机载视觉感知彼此的相对位置。在这些实验中,提出的博弈论规划器再次优于 MPC 对手,其中无人机的速度达到 ${1.25\,}\mathrm{{m}}/\mathrm{{s}}$。
更新日期:2020-10-01
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