Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Abstract

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

A Review on Path Planning Algorithms for Mobile Robots

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A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

“…The optimization algorithms for pathfinding for ground robotics [20][21][22][23][24], aerial vehicles [25][26][27], and underwater vehicles [28,29] includes a wide range of applications. The most well-known applications for autonomous vehicles are obstacle avoidance, path planning, localization, navigation, sensing, and communication, which works on preessential maps related to the environment; they also play a vital role in communication relay, aviation industry for surveillance, and loitering dominated missions.…”

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

“…The fundamental motivation for using meta-heuristic algorithms in this controlling and optimization problem is reliable optimization through the use of a local search strategy and the randomness in their evolutionary processes to find optimal or near-optimal solutions. However, this does not always result in meta-heuristics finding compelling or optimal solutions, as was shown in several cases [33,44,45]. In such a context, exploration and exploitation constitute the fundamental and important processes of meta-heuristic algorithms.…”