To handle the problem of low detection accuracy and missed detection caused by dense detection objects, overlapping, and occlusions in the scenario of complex construction machinery swarm operations, this paper proposes a multi-object detection method based on the improved YOLOv4 model. Firstly, the K-means algorithm is used to initialize the anchor boxes to improve the learning efficiency of the depth features of construction machinery objects. Then, the pooling operation is replaced with dilated convolution to solve the problem that the pooling layer reduces the resolution of feature maps and causes a high missed detection rate. Finally, focus loss is introduced to optimize the loss function of YOLOv4 to improve the imbalance of positive and negative samples during the model training process. To verify the effectiveness of the above optimizations, the proposed method is verified on the Pytorch platform with a self-build dataset. The experimental results show that the mean average precision(mAP) of the improved YOLOv4 model for multi-object detection of construction machinery can reach 97.03%, which is 2.16% higher than that of the original YOLOv4 detection network. Meanwhile, the detection speed is 31.11 fps, and it is reduced by only 0.59 fps, still meeting the real-time requirements. The research lays a foundation for environment perception of construction machinery swarm operations and promotes the unmanned and intelligent development of construction machinery swarm operations.

Accurate energy flow results are the premise of excavator energy-saving control research. Only through an accurate energy flow analysis based on operating data can a practical excavator energy-saving control scheme be proposed. In order to obtain the excavator’s accurate energy flow, the excavator components’ performance and operating data requirements are obtained, and the experimental schemes are designed to collect it under typical working conditions. The typical working condition load is reconstructed based on wavelet decomposition, harmonic function, and theoretical weighting methods. This paper analyzes the excavator system’s energy flow under the typical working condition load. In operation conditions, the output energy of the engine only accounts for 50.21% of the engine’s fuel energy, and the actuation and the swing system account for 9.33% and 4%, respectively. In transportation conditions, the output energy of the engine only accounts for 49.80% of the engine’s fuel energy, and the torque converter efficiency loss and excavator driving energy account for 15.09% and 17.98%, respectively. The research results show that the energy flow analysis method based on typical working condition load can accurately obtain each excavator component’s energy margin, which provides a basis for designing energy-saving schemes and control strategies.

To overcome the difficulty of collecting the working resistance and working trajectory of a wheel loader, this paper constructs a statics model of the bucket working resistance and a kinematics model of the working trajectory in the shoveling process and analyzes the key parameters of measuring the working resistance and working trajectory. Based on this, a working resistance and working trajectory acquisition strategy is proposed. To verify the effectiveness of the acquisition strategy, the in-service operation data of fine sand and loose soil shoveled by the wheel loader are collected and analyzed. Then, the test-fitted working resistance and working trajectory are obtained, and the working trajectory is input into the RecurDyn–EDEM co-simulation model to obtain the simulation-fitted working resistance. Considering the complex working conditions of the wheel loader, it is difficult to obtain accurate working resistance, and the actual working resistance is also a relative value. Therefore, a strong correlation between the two curves indicates that the acquisition strategy of the wheel loader’s working trajectory and working resistance proposed in this paper is feasible.

The bucket fill factor is a core evaluation indicator for the optimization of the loader’s autonomous shoveling operation. Accurately predicting the bucket fill factor of the loader after different excavation trajectories is fundamental for optimizing the loader’s efficiency and energy cost. Therefore, this paper proposes a method for predicting the bucket fill factor of the loader based on the three-dimensional information of the material surface. Firstly, the co-simulation model of loader shoveling material is established based on the multi-body dynamics software RecurDyn and the discrete element method software (DEMS) EDEM, and the co-simulation is conducted under different excavation trajectories. Then, the three-dimensional material surface information before shovel excavation is obtained from DEMS, and the surface function of the material contour is fitted based on the corresponding shovel excavation trajectory information. Meanwhile, the volume of the material excavated by the loader is obtained by the numerical integration method, and it is divided by the rated bucket volume to obtain the estimated bucket fill factor. Finally, the actual volume of the material after the shovel excavation is divided by the rated bucket volume to obtain the accurate bucket fill factor. Based on this, the prediction model of the bucket fill factor is built. The experimental results show that the proposed method is feasible, with a maximum error of 4.3%, a root mean square error of 0.025 and an average absolute error of 0.021. The research work lays the foundation for predicting the bucket fill factor of construction machinery such as loaders and excavators under real working conditions, which is conducive to promoting the development of autonomous, unmanned, and intelligent construction machinery.

No abstract