MULTI ROBOT PATH PLANNING ALGORITHMS: A SURVEY
Keywords:
Bacteria forging Optimization (BFO), Ant Colony Optimization (ACO), Particle Swam Optimization (PSO), Ant Colony System (ACS), Robot path planning (RPP), Genetic algorithms (GA)Abstract
To find the optimal path by interacting with multiple robots is the main research area in field of robotics. The task is to find the global optimal path with a minimum amount of computation time. Path planning has numerous application like industrial robotics, to design autonomous system etc. In this paper, we survey on three most recent algorithms namely Bacteria forging Optimization (BFO), Ant Colony Optimization (ACO), Particle Swam Optimization (PSO) that can apply on multiple robots to find the optimal path. The main feature of BFO is the chemotactic movement of a virtual bacterium that is helpful to investigate the all the possible path and finally arrived at optimal solution. In Ant Colony System (ACS) algorithm is integration of heuristic and visibility equation of state transition rules for finding the optimal path. PSO is stochastic optimization technique inspired by social behaviour of bird flocking. The solutions, called particles, fly through the problem space by some set of the rules.
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