Aiming to address the A* algorithm’s issues of traversing a large number of nodes, long search times, and large turning angles in path planning, a strategy for multiple improvements to the A* algorithm is proposed. Firstly, the calculation of the heuristic function is refined by utilizing the Octile distance instead of traditional distance, which more accurately predicts the optimal path length. Additionally, environmental constraints are introduced to adaptively adjust the weight of the heuristic function, balancing the trade-off between search speed and path length. Secondly, the bidirectional jump point search method is integrated, allowing simultaneous path searches from both directions. This significantly reduces path search times and the number of nodes traversed. Finally, the path undergoes two rounds of smoothing using a path smoothing strategy until the final path is generated. To validate the effectiveness of the improved A* algorithm, simulations are conducted on ten types of grid maps. Results demonstrate that the improved A* algorithm markedly decreases path search times while maintaining path length, with greater speed improvements observed as the map size increases. Furthermore, the improved algorithm is applied in experiments with mobile robots, achieving significant reductions in average path search times of 79.04% and 37.41% compared to the traditional A* algorithm and the JPS algorithm, respectively. This enhancement effectively meets the requirements for rapid path planning in mobile robotics applications.

Poultry behaviors reflect the health status of poultry. For four behaviors of laying hens, such as standing, lying, feeding, and grooming, four deep learning methods for recognition were compared in this paper, as Efficientnet-YoloV3, YoloV4-Tiny, YoloV5, and Faster-RCNN. First, the behavior detection dataset was produced based on the monitoring video data. Then, four algorithms, Efficientnet-YoloV3, YoloV4-Tiny, YoloV5, and Faster-RCNN, were used for training respectively. Finally, using the validation set for recognition, we got the mAP values for the four algorithms: Efficientnet-YoloV3 had mAP values of 81.82% (standing), 88.36% (lying), 98.20% (feeding), 77.30% (grooming), and its FPS values were 9.83 in order; YoloV4-Tiny had mAP values of 65.50% (standing), 78.40% (lying), 94.51% (feeding), 62.70% (grooming), and their FPS values were 14.73 successively; YoloV5 had mAP values of 97.24% (standing), 98.61% (lying), 97.43% (feeding), 92.33% (grooming), and their FPS values were 55.55 successively; Faster-RCNN had mAP values were 95.40% (standing), 98.50% (lying), 99.10% (feeding), and 85.40% (grooming), and their FPS values were 3.54 respectively. The results showed that the YoloV5 algorithm was the optimal algorithm among the four algorithms and could meet the requirements for real-time recognition of laying hens’ behavior.

To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network’s output to ensure the consistency of each channel’s output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification.