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arXiv · 2112.04639

Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics

Zhichao Li·Thai Duong·Nikolay Atanasov
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Abstract

Safe autonomous navigation in unknown environments is an important problem for mobile robots. This paper proposes techniques to learn the dynamics model of a mobile robot from trajectory data and synthesize a tracking controller with safety and stability guarantees. The state of a rigid-body 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. We enable adaptive tracking of a desired path, subject to safety constraints obtained from obstacle distance measurements. The trade-off between the robot's energy and the distance to safety constraint violation is used to adaptively govern a reference pose along the desired path. Our safe adaptive controller is demonstrated on a simulated hexarotor robot navigating in an unknown environments.

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Zhichao Li, Thai Duong, Nikolay Atanasov. 2021-12-09. Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics. https://arxiv.org/abs/2112.04639

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