Implementation and Comparison between PSO and BAT Algorithms for Path Planning with Unknown Environment

Abstract—The main challenges of robotics are its automation and detection capability. Robotic Path Planning is one of the main problems that deal with computation of a collision-free path for the given robot, along with the given map on which it operates. This paper focuses on Path planning and exploration problem in unknown environment with multi-robot system. Here we have dealt with two traditional nature-inspired algorithms namely Particle Swarm Optimization and Bat Algorithm for movement decision making with unknown environment and last comparing them different features i.e. moves, time, coverage and energy.

Keywords—BAT, Bio-inspired Algorithm, Multi-Robot system, Path Planning, PSO.

INTRODUCTION

Every automated and intelligent robotic system uses planning to decide the motion of root and real world. Robotics is an extremely inter-disciplinary area that takes inputs from numerous disciples and fields with varying complexities. Soft Computing Tools and Techniques are finding increasing application in the field of robotics. Many of the robotic problems like planning, coordination etc. are complex problems that are now-a-days solved using the most sophisticated soft computing tools. The use of Soft Computing tools and techniques lies at each of the steps in multiple ways. At the time of information processing Soft Computing tools not only help in pre-processing and noise removal, but help in landmark and object detection as well. Similarly many Soft Computing tools are used for the map building and path planning. The high-end intelligent planning involving many specific problems such as multi-robot coordination again involves algorithms from the domain of Artificial Intelligent and Soft Computing. In many real-world applications, workspace of robots often involves various danger sources that robots must evade, such as fire in rescue mission, landmines and enemies in war field [1].
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