Robot Path Planning Based on Gray Wolf Algorithm under Road Network Constraints
Due to the large number of initial path groups and many redundant individuals,generally,the problems of low efficiency and low reliability exist in path planning process.Therefore,based on gray wolf algorithm,this paper presented a method for robot path planning under road network constraints.Firstly,multiple sensors were used to col-lect road data,including vehicle and environment.And then,the speed,traffic volume and density were calculated.Secondly,information conservation theory was adopted to calculate the traffic data smoothly.Meanwhile,negative ex-ponential function was used to design the evidence theory reliability.Moreover,Kalman filter was introduced to achieve the fusion of traffic data,thus forming a complete architecture of road network.Furthermore,gray wolf algo-rithm was used to plan the path of robot.The three wolves with the highest fitness in wolfpack were regarded as the head wolves.Finally,a mathematical model was built by searching for,encircling prey and attacking prey.After the o-rientation of gray wolf was updated,the robot path adaptive planning was completed.The experimental results show that the proposed method has strong timeliness,and can achieve optimal path planning in static and dynamic environ-ments.In addition,the method can find the optimal path only after 4 iterations in dynamic environment,while provi-ding technical help for efficient application of robots.
Road network constraintsGray Wolf algorithmRobot movementPath planningData-aware
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