Robot obstacle avoidance path planning based on improved convolutional neural network
Due to the inability of the robot to update its position direction and coordinates in a timely manner during its movement,the target is not the global minimum point,resulting in the robot being unable to effectively avoid obstacles.Therefore,a robot obstacle avoidance path planning method based on improved convolutional neural networks is proposed.Using a bilinear interpolation method based on improved convolutional neural network,calculate the coordinates of the target point.Evaluate the function using the dynamic window method and calculate the extension distance.Construct a maximum loss function,which maximizes the degree of feature aggregation within the category and the differences between categories through decreasing learning.Estimate the robot's position through its movement in the environment,calculate the robot's translation speed and angular velocity,and update the robot's position direction and coordinates.Construct an improved rejection function,calculate the distance from the center point of the neuron to the center point of the target neuron,and plan the obstacle avoidance path.The experimental results show that this method can avoid all obstacles,and the distance between the planned starting point and target point is consistent with the actual distance.
improving convolutional neural networksrobot obstacle avoidancepath planningglobal minimum point
万方数据
维普
