NMPC-LBF: Nonlinear MPC with Learned Barrier Function for Decentralized Safe Navigation of Multiple Robots in Unknown Environments

TL;DR

This paper introduces NMPC-LBF, a decentralized control method that uses learned barrier functions via neural networks to ensure safe navigation of multiple robots in unknown environments, preventing collisions with obstacles and each other.

Contribution

The paper presents a novel decentralized NMPC approach incorporating real-time learned barrier functions for safe multi-robot navigation in unknown environments.

Findings

Effective collision avoidance demonstrated in simulations.

Successful real-world implementation on Turtlebot3 robots.

Neural network effectively learns barrier functions from sensor data.

Abstract

In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles and other robots, we use a Deep Neural Network (DeepNN) running in real-time on each robot to learn the Barrier Function (BF) only from the robot's LiDAR and odometry measurements. The DeepNN is trained to learn the BF that separates safe and unsafe regions. We implemented our proposed method on simulated and actual Turtlebot3 Burger robot(s) in different scenarios. The implementation results show the effectiveness of the NMPC-LBF method at ensuring safe navigation of the robots.