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

by   Zhichao Li, et al.

Stability and safety are critical properties for successful deployment of automatic control systems. As a motivating example, consider autonomous mobile robot navigation in a complex environment. A control design that generalizes to different operational conditions requires a model of the system dynamics, robustness to modeling errors, and satisfaction of safety constraints, such as collision avoidance. This paper develops a neural ordinary differential equation network to learn the dynamics of a Hamiltonian system from trajectory data. The learned Hamiltonian model is used to synthesize an energy-shaping passivity-based controller and analyze its robustness to uncertainty in the learned model and its safety with respect to constraints imposed by the environment. Given a desired reference path for the system, we extend our design using a virtual reference governor to achieve tracking control. The governor state serves as a regulation point that moves along the reference path adaptively, balancing the system energy level, model uncertainty bounds, and distance to safety violation to guarantee robustness and safety. Our Hamiltonian dynamics learning and tracking control techniques are demonstrated on simulated hexarotor and quadrotor robots navigating in cluttered 3D environments.


page 2

page 3

page 5

page 6

page 8

page 10

page 13

page 14


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

Safe autonomous navigation in unknown environments is an important probl...

Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

Reliable autonomous navigation requires adapting the control policy of a...

Fast and Safe Path-Following Control using a State-Dependent Directional Metric

This paper considers the problem of fast and safe autonomous navigation ...

Safety-Augmented Operation of Mobile Robots Using Variable Structure Control

The design process and complexity of existing safety controls are heavil...

Design of a Supervisory Control System for Autonomous Operation of Advanced Reactors

Advanced reactors deployed in the coming decades will face deregulated e...

Gaussian Processes Model-based Control of Underactuated Balance Robots

Ranging from cart-pole systems and autonomous bicycles to bipedal robots...

Safe Learning Reference Governor for Constrained Systems with Application to Fuel Truck Rollover Avoidance

This paper proposes a learning reference governor (LRG) approach to enfo...

Please sign up or login with your details

Forgot password? Click here to reset