Safe and efficient motion planning for autonomous systems using Hamilton-Jacobi reachability

Abstract

A method and system are provided for the safe motion planning of an autonomous system using a neural network-based approximation of a Hamilton-Jacobi value function (HJ value function). The method involves computing signed distance fields (SDFs) from occupancy grid maps, deriving time differences between SDFs, and using these differences as input for a hypernetwork that generates parameters for a main network. The main network computes a residual of the HJ value function, which is modified using a leaky rectified linear unit and combined with a selected SDF to form an intermediate value function. A state-dependent slack function is added to produce a final HJ value function, which is used as a safety constraint in motion planning.The system enables real-time, adaptive and robust planning in dynamic and partially observable environments.