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

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