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

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

Overview of Optimization Techniques in Geometric Path Planning for Mobile Robots with a new smooth PSO-IPF design

In this paper, we explore the practical implications of our research, offering significant advantages to the autonomous and robotics sector. Our focus revolves around enhancing geometric path planning for mobile robots, a pivotal aspect of automation. Notably, we not only delve into how to formulate optimal navigation problems while considering practical constraints and applications, but we also introduce a novel Smoothed PSO-IPF algorithm. This algorithm serves as an illustrative example of an innovative and context-specific approach to addressing navigation challenges. It furnishes engineers and practitioners in the field with a comprehensive framework for designing navigational solutions. By presenting the PSO-IPF method as a hybrid approach, we effectively bridge the gap between classical and reactive methods. Consequently, it leads to enhanced navigation efficiency, reduced collisions, and heightened mobile robot reliability. This innovation not only optimizes navigation issues but also underscores its potential for diverse applications across various industries.</p