Multi-objective path planning in discrete space
Finding a path for a robot which is near to natural looking paths is a challenging problem in motion planning. This paper suggests two single and multi-objective optimization models focusing on length and clearance of the path in discrete space. Considering the complexity of the models and potency of evolutionary algorithms we apply a genetic algorithm with NSGA-II framework for solving the problems addressed in the models. The proposed algorithm uses an innovative family of path refiner operators, in addition to the standard genetic operators. The new operators intensify explorative power of the algorithm in finding Pareto-optimal fronts in the complicated path planning problems such as narrow passages and clutter spaces. Finally, we compare efficiency of the refiner operators and the algorithm with PSO and A* algorithms in several path planning problems.
path planning multi-objective optimization genetic algorithm computational geometry