NMPCB: A Lightweight and Safety-Critical Motion Control Framework for Ackermann Mobile Robot

TL;DR

This paper introduces NMPCB, a lightweight, safety-critical motion control framework for Ackermann mobile robots that combines neural network path planning with a control barrier function-based MPC to ensure real-time safety and efficiency.

Contribution

The paper presents a novel integrated framework using neural networks and control barrier functions for safe, real-time motion control of Ackermann robots, reducing computation while maintaining safety.

Findings

Framework achieves real-time performance in simulations and experiments.

Ensures safety through control barrier functions with reduced computation.

Validates effectiveness in multi-obstacle environments.

Abstract

In multi-obstacle environments, real-time performance and safety in robot motion control have long been challenging issues, as conventional methods often struggle to balance the two. In this paper, we propose a novel motion control framework composed of a Neural network-based path planner and a Model Predictive Control (MPC) controller based on control Barrier function (NMPCB) . The planner predicts the next target point through a lightweight neural network and generates a reference trajectory for the controller. In the design of the controller, we introduce the dual problem of control barrier function (CBF) as the obstacle avoidance constraint, enabling it to ensure robot motion safety while significantly reducing computation time. The controller directly outputs control commands to the robot by tracking the reference trajectory. This framework achieves a balance between real-time performance and safety. We validate the feasibility of the framework through numerical simulations and real-world experiments.