HPTune: Hierarchical Proactive Tuning for Collision-Free Model Predictive Control

TL;DR

HPTune introduces a hierarchical proactive parameter tuning framework for model predictive control that improves collision avoidance and motion planning efficiency by evaluating both executed and non-executed actions.

Contribution

The paper presents a novel hierarchical proactive tuning method that combines fast and slow evaluation levels, enhancing MPC performance in complex environments.

Findings

HPTune outperforms baseline schemes in simulator tests.

It enables safer and more agile collision avoidance.

The framework effectively utilizes obstacle velocity data from Doppler LiDAR.

Abstract

Parameter tuning is a powerful approach to enhance adaptability in model predictive control (MPC) motion planners. However, existing methods typically operate in a myopic fashion that only evaluates executed actions, leading to inefficient parameter updates due to the sparsity of failure events (e.g., obstacle nearness or collision). To cope with this issue, we propose to extend evaluation from executed to non-executed actions, yielding a hierarchical proactive tuning (HPTune) framework that combines both a fast-level tuning and a slow-level tuning. The fast one adopts risk indicators of predictive closing speed and predictive proximity distance, and the slow one leverages an extended evaluation loss for closed-loop backpropagation. Additionally, we integrate HPTune with the Doppler LiDAR that provides obstacle velocities apart from position-only measurements for enhanced motion predictions, thus facilitating the implementation of HPTune. Extensive experiments on high-fidelity simulator demonstrate that HPTune achieves efficient MPC tuning and outperforms various baseline schemes in complex environments. It is found that HPTune enables situation-tailored motion planning by formulating a safe, agile collision avoidance strategy.