论文标题
GLAS：端到端学习的多机器人运动计划的全球到本地安全自治综合
GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
论文作者
论文摘要
我们提出GLAS：全球到本地自治综合，这是一种用于多机器人运动计划的可证明是安全的，自动化的分布式策略生成。我们的方法结合了避免局部最小值的集中计划的优势，并具有分散的可扩展性和分布式计算控制器的优势。特别是，我们的合成政策只需要附近邻居和障碍的相对状态信息，并计算出可证明的安全行动。我们的方法具有三个主要组成部分：i）我们使用全球规划师生成了示范轨迹，并从中提取本地观察结果，ii）我们使用深层模仿学习来学习一个可以在线上有效运行的分散政策，iii）我们引入了一个新型的可微分模块，以确保无冲突的操作，从而允许最终到端的政策培训。我们的数值实验表明，在广泛的机器人和障碍物密度之间，我们的策略的成功率高于最佳相互碰撞的最佳碰撞率高20％。我们在空中群上演示了我们的方法，实时执行低端微控制器的政策。
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.
