Robotic path planning using hybrid genetic algorithm particle swarm optimisation

The problem of robotic path planning has always attracted the interests of a significantly large number of researchers due to the various constraints and issues related to it. The optimization in terms of time and path length and validity of the non-holonomic constraints, especially in large sized maps of high resolution, pose serious challenges for the researchers. In this paper we propose Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) algorithm for solving the problem. Diversity preservation measures are introduced in this applied evolutionary technique. The novelty of the algorithm is threefold. Firstly the algorithm generates paths of increasing complexity along with time. This ensures that the algorithm generates the best path for any type of map. Secondly the algorithm is efficient in terms of computational time which is done by introducing the concept of momentum based exploration in its fitness function. The indicators contributing to fitness function can only be measured by exploring the path represented. This exploration is vague at start and detailed at the later stages. Thirdly the algorithm uses a multi-objective optimization technique to optimize the total path length, the distance from obstacle and the maximum number of turns. These multi-objective parameters may be altered according to the robot design.