The complexity of robot motion planning
The complexity of robot motion planning
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Planning Algorithms
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
Mobile robot path planning using exact cell decomposition and potential field methods
WSEAS Transactions on Circuits and Systems
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Generalizing the improved run-time complexity algorithm for non-dominated sorting
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.