In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goal of the robot is to safely collect observations of the disturbance and construct an accurate estimate of the underlying disturbance function. We use Gaussian Process (GP) to get an estimate of the disturbance from data with a high-confidence bound on the regression error. Furthermore, we use neural Contraction Metrics to derive a tracking controller and the corresponding high-confidence uncertainty tube around the nominal trajectory planned for the robot, based on the estimate of the disturbance. From the robustness of the Contraction Metric, error bound can be pre-computed and used by the motion planner such that the actual trajectory is guaranteed to be safe. As the robot collects more and more observations along its trajectory, the estimate of the disturbance becomes more and more accurate, which in turn improves the performance of the tracking controller and enlarges the free space that the robot can safely explore. We evaluate the proposed method using a carefully designed environment with a ground vehicle. Results show that with the proposed method the robot can thoroughly explore the environment safely and quickly.
翻译:在本文中,我们考虑了使用机器人探索一个具有未知的、以状态为依存的扰动功能的环境,同时避免某些禁区的问题。机器人的目标是安全地收集扰动的观测结果,并对潜在的扰动功能作出准确的估计。我们使用高山进程(GP)从以回归误差为约束的高度自信数据中获得扰动的估计。此外,我们使用神经控制仪,根据对扰动的估计,在为机器人计划的名义轨道周围产生跟踪控制器和相应的高信任不确定性管。从电磁仪的坚固性来看,误差可以预先计算,并被运动规划员使用,从而保证实际轨迹的安全。随着机器人沿轨迹收集越来越多的观测结果,扰动估计会越来越准确,这反过来会提高跟踪控制器的性能,扩大机器人可以安全探索的自由空间。我们用一种精心设计的地面飞行器环境来评估拟议的方法。结果显示,用拟议的方法可以使机器人能够安全地、迅速地彻底地探索环境。