Global path planning with integration of B-spline technique and genetic algorithm
A path planning method integrating B-spline technique and genetic algorithm was proposed,aiming at the path planning problem of robots in complex obstacle environments.Firstly,a strategy based on the multi-objective A* algorithm for generating path-type value points as well as inversing the control points was designed to generate a high-quality initial population,so as to increase the population diversity and improve the early convergence speed of the algorithm.Secondly,a novel fitness function was designed by integrating the continuity,safety and shortest of path,and the fitness value of each path was calculated.Then,the adaptive strategy was introduced to adjust the crossover and mutation operators to increase the diversity of individuals and avoid premature convergence to local optimal solutions.Finally,simulation experiments of the proposed algorithm were conducted based on MATLAB.The experimental results in complex static environment showed that the length of the robot traveling path generated by the proposed algorithm was reduced by an average of 8.22％ and 2.15％,and the prematurity was reduced by an average of 88.31％ and 77.08％,compared with the paths generated by GABE and IPSO-SP methods.And the paths had a second-order continuum derivability (i.e.,C2 continuum),which improved the robot's traveling stability.Simultaneously,the proposed algorithm was verified to be able to complete the path planning efficiently in real environments through navigation experiments by combining with the robot operation platform.
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