Abstract
The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.
Highlights
Unmanned ground vehicles (UGVs) are often used in the field of unmanned operation, especially in repetitive and single-factory environments [1,2]
Unmanned ground vehicles have been gradually applied to the indoor environment
In the complex indoor environment, unmanned vehicles often encounter obstacles not indicated in the built-in map during operation
Summary
Unmanned ground vehicles (UGVs) are often used in the field of unmanned operation, especially in repetitive and single-factory environments [1,2]. How to sense obstacles in time and avoid obstacles is a hot issue in the research of indoor tracking unmanned vehicles. Based on the lidar data, combined with the vehicle position, obstacle position, vehicle operation capability, and global environmental restrictions, the optimized path was generated, and the path was updated in real time through the detection data.
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