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GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2020-07-01 , DOI: 10.1109/lra.2020.2994035
Benjamin Riviere , Wolfgang Honig , Yisong Yue , Soon-Jo Chung

We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time.

中文翻译:

GLAS:具有端到端学习的多机器人运动规划的全局到局部安全自主综合

我们提出了 GLAS:全局到本地自治综合,这是一种可证明安全的自动化分布式策略生成,用于多机器人运动规划。我们的方法结合了避免局部最小值的集中规划的优势与可扩展性和分布式计算的分散控制器的优势。特别是,我们的综合策略只需要附近邻居和障碍物的相对状态信息,并计算一个可证明安全的动作。我们的方法有三个主要组成部分:i)我们使用全局规划器生成示范轨迹并从中提取局部观察,ii)我们使用深度模仿学习来学习可以在线高效运行的分散策略,以及 iii)我们引入了一个新的可微分安全模块,确保无碰撞运行,从而允许进行端到端的政策培训。我们的数值实验表明,在广泛的机器人和障碍物密度范围内,我们的策略比最佳相互碰撞避免 ORCA 的成功率高 20%。我们在空中群中演示了我们的方法,实时执行低端微控制器的策略。
更新日期:2020-07-01
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