Contrastive Analysis of Path Planning Algorithms for Mobile Robots-A case study

Abstract

Path planning is one of the most challenging aspects of mobile robots, which has uses in everything from self-driving cars to automating warehouses. This essay gives a thorough look at five well-known path planning algorithms: Particle Swarm Optimisation (PSO), Genetic Algorithms (GA), A*, Dijkstra, and Rapidly-exploring Random Trees (RRT). Our study rates these algorithms on how well they work, how well they perform, and how well they can change to different situations. The ability to optimize in PSO is talked about, along with the genetic-inspired search in GA, the heuristic-driven method in A*, the dependability of Dijkstra, and the probabilistic exploration in RRT. Path length, execution time, and answer quality are just some of the parameters that are carefully looked at. Experimentation and modelling studies are done to make sure that algorithms can be used in real life. Our results give researchers and practitioners important information about how these algorithms compare in terms of performance. This lets them choose the best method for their specific robotic uses.