We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments. First, we use stochastic model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. Second, recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution, which are trained on uncertainty outputs of various simultaneous localization and mapping algorithms. When two or more robots are in communication range, these uncertainties are then updated using a distributed Kalman filtering approach. Lastly, a Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal. Our complete methods are demonstrated on a ground and aerial robot simultaneously (code available at: https://github.com/AlexS28/SABER).

中文翻译：

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SABRE：数据驱动的运动规划器，用于自主导航异构机器人

我们提出了一个端到端的在线运动规划框架，该框架使用数据驱动的方法将异构机器人团队导航到全球目标，同时避免不确定环境中的障碍。首先，我们使用随机模型预测控制 (SMPC) 来计算满足机器人动力学的控制输入，并在具有机会约束的避障过程中考虑不确定性。其次，循环神经网络用于提供 SMPC 有限时间范围解决方案中考虑的未来状态不确定性的快速估计，这些网络在各种同时定位和映射算法的不确定性输出上进行训练。当两个或更多机器人处于通信范围内时，这些不确定性将使用分布式卡尔曼滤波方法进行更新。最后，深度 Q 学习代理用作高级路径规划器，为 SMPC 提供目标位置，使机器人朝着所需的全局目标移动。我们的完整方法在地面和空中机器人上同时演示（代码可在：https://github.com/AlexS28/SABER）。