Safe autonomous navigation in unknown environments is an important problem for ground, aerial, and underwater robots. This paper proposes techniques to learn the dynamics models of a mobile robot from trajectory data and synthesize a tracking controller with safety and stability guarantees. The state of a mobile robot usually contains its position, orientation, and generalized velocity and satisfies Hamilton's equations of motion. Instead of a hand-derived dynamics model, we use a dataset of state-control trajectories to train a translation-equivariant nonlinear Hamiltonian model represented as a neural ordinary differential equation (ODE) network. The learned Hamiltonian model is used to synthesize an energy-shaping passivity-based controller and derive conditions which guarantee safe regulation to a desired reference pose. Finally, we enable adaptive tracking of a desired path, subject to safety constraints obtained from obstacle distance measurements. The trade-off between the system's energy level and the distance to safety constraint violation is used to adaptively govern the reference pose along the desired path. Our safe adaptive controller is demonstrated on a simulated hexarotor robot navigating in unknown complex environments.
翻译:未知环境中的安全自主导航是地面、空中和水下机器人的一个重要问题。 本文提出从轨迹数据中学习移动机器人动态模型的技术, 并将跟踪控制器与安全和稳定保证结合起来。 移动机器人的状态通常包含其位置、 方向和通用速度, 并满足汉密尔顿运动方程式。 我们使用一套来自手动的动态模型, 使用一套州控轨道数据集来训练一个翻译- 等同非线性汉密尔顿模型, 作为神经普通差分方程( ODE) 网络 。 所学的汉密尔顿模型用来合成一个基于能量的被动控制器, 并创造一些条件, 保证安全地调节所需的参考方形。 最后, 我们允许根据障碍距离测量获得的安全限制, 以适应性的方式跟踪一条理想的道路。 系统能源水平和安全约束距离之间的权衡, 用于适应性地管理所要沿路径的参考。 我们的安全适应性控制器在未知复杂环境中的模拟六价机器人导航。