Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.

Obstacle avoidance, researching goal, and the path planning are the most important tasks for an autonomous mobile robot which moves in an uncertain environment, this paper presents a method of a fuzzy controller to control the robot to avoid all fixed and mobile obstacles. The objective is the control and programming of a mobile robot with obstacle avoidance using fuzzy logic. This robot can be controlled by a human-machine interface or by a keyboard. Communication between the human-machine interface and Robot is done by using wireless communication. Ultrasonic sensors and infrared sensors will be used to measure the distances between the robot and the obstacles in different directions. This robot can avoid collision with obstacles by intelligent reasoning using fuzzy logic.

Path planning is one of the most critical tasks in mobile robotics. This paper proposes a hybridization of two algorithms for optimizing the trajectory planning a mobile robot and ensuring its navigation in a partially known environment containing static obstacles. The Grey Wolf Optimization (GWO) algorithm, which has some challenges such as slow convergence and low accuracy, is combined with the Particle Swarm Optimization (PSO) algorithm for its high convergence speed and accuracy. The hybrid PSO-GWO algorithm is proposed so that the optimal trajectory search procedure balances global and local search capabilities, taking advantage of the capacities of both GWO and PSO approaches. The validity of PSO-GWO performance has been tested for various scenarios using the mobile robot Khepera-iv and the simulation software of MATLAB and V-REP. Then, it has revealed that this method can offer a better search for an optimal trajectory when compared to other algorithms such as GWO, PSO, and Fuzzy-Logic. For implementing the PSO-GWO algorithm on Khepera-iv, a simple Extended Kalman Filter (EKF) is developed to locate the mobile robot, in its realistic environment, by fusing data from different sensors. Copyright © 2021 Praise Worthy Prize - All rights reserved.