Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles

Frontiers in Robotics and AI

Robotics and AI

Samaneh-Alsadat Saeedinia¹  Zahra Roozbehi²  Mahsa Mohaghegh³ 

[1] School of Electrical Engineering, University of Science and Technology (IUST), Tehran, Iran;School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland, New Zealand;School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland, New Zealand;Faculty of Design and Creative Technologies, AUT, Auckland, New Zealand;

关键词: navigation;    optimization;    MPC;    stability;    neural network;    dynamic environment;   

DOI  :  10.3389/frobt.2023.1226028

 received in 2023-05-20, accepted in 2023-07-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: The challenge of navigating a Mobile robot in dynamic environments has grasped significant attention in recent years. Despite the available techniques, there is still a need for efficient and reliable approaches that can address the challenges of real-time near optimal navigation and collision avoidance.Methods: This paper proposes a novel Log-concave Model Predictive Controller (MPC) algorithm that addresses these challenges by utilizing a unique formulation of cost functions and dynamic constraints, as well as a convergence criterion based on Lyapunov stability theory. The proposed approach is mapped onto a novel recurrent neural network (RNN) structure and compared with the CVXOPT optimization tool. The key contribution of this study is the combination of neural networks with model predictive controller to solve optimal control problems locally near the robot, which offers several advantages, including computational efficiency and the ability to handle nonlinear and complex systems.Results: The major findings of this study include the successful implementation and evaluation of the proposed algorithm, which outperforms other methods such as RRT, A-Star, and LQ-MPC in terms of reliability and speed. This approach has the potential to facilitate real-time navigation of mobile robots in dynamic environments and ensure a feasible solution for the proposed constrained-optimization problem.

【 授权许可】

Unknown   
Copyright © 2023 Mohaghegh, Saeedinia and Roozbehi.