Emergence of Multi Objective Genetic Algorithm in Robot Path Planning –a Survey

In the field of robotics, before the conception of any approach to solve robot path planning, it would be the procedure to derive a model for the problem and solution theoretically, then going for simulations to verify the derived model and then implemented and validated in the real time applications, by taking into account many real time factors. This survey studies the evolution of implementation of genetic algorithm principle in robot path planning to real multi objective genetic algorithm. The classical approaches may produce the best solution for the given problem space which consumes the huge amount of computational power and memory. The computational complexity in turn time complexity increases proportional to the complexity of environment and configuration of robot. Whereas it is evident from the research domain, the heuristic approaches emerged with better approaches to overcome the disadvantages of the classical approaches. The genetic algorithms playing the important role in finding solution like robot path planning which are considered as NP-hard problem. The refinement in the Genetic Algorithm lead through the another leap in robot path planning considering all the objective functions as fitness functions instead of one of the objectives as fitness function and other objectives are added as constraints. The Multi Objective Genetic Algorithm implementation is generating the challenging performance either with some variations in the implementation or with hybridization of other methodologies.