Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments

TL;DR

This paper introduces a learning-based method using hypernetworks to estimate maximal safe sets for MPC-based local trajectory planning in unknown environments, enhancing safety and success rates.

Contribution

It proposes a novel neural approach employing hypernetworks and HJ reachability for real-time safe set estimation in nonlinear dynamic systems.

Findings

Success rate increased by up to 52% in simulations

Achieved real-time performance with comparable speed to baselines

Successfully demonstrated obstacle avoidance on a physical robot

Abstract

This paper presents a novel learning-based approach for online estimation of maximal safe sets for local trajectory planning in unknown static environments. The neural representation of a set is used as the terminal set constraint for a model predictive control (MPC) local planner, resulting in improved recursive feasibility and safety. To achieve real-time performance and desired generalization properties, we employ the idea of hypernetworks. We use the Hamilton-Jacobi (HJ) reachability analysis as the source of supervision during the training process, allowing us to consider general nonlinear dynamics and arbitrary constraints. The proposed method is extensively evaluated against relevant baselines in simulations for different environments and robot dynamics. The results show an increase in success rate of up to 52% compared to the best baseline while maintaining comparable execution speed. Additionally, we deploy our proposed method, NTC-MPC, on a physical robot and demonstrate its ability to safely avoid obstacles in scenarios where the baselines fail.