The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

Roadside perception can significantly enhance the safety of autonomous vehicles by extending their perceptual capabilities beyond the visual range and addressing occluded regions. However, current state-of-the-art vision-based roadside detection methods exhibit high accuracy on labeled scenes but perform poorly on new scenes. This limitation arises because roadside cameras remain stationary after installation and can only gather data from a single scene, leading the algorithm to overfit these roadside backgrounds and camera positions. To tackle this issue, we propose an innovative Scenario Generalization Framework for Vision-based Roadside 3D Object Detection, called SGV3D. Specifically, we utilize a Background-suppressed Module (BSM) to reduce background overfitting in vision-centric pipelines by diminishing background features during the 2D to bird’s-eye-view projection. Furthermore, by introducing the Semi-supervised Data Generation Pipeline (SSDG) that employs unlabeled images from new scenes, we generate diverse foreground instances with varying camera poses, mitigating the risk of overfitting to specific camera positions. Experiments conducted on two large-scale roadside benchmarks demonstrate that SGV3D, with only a minimal increase in latency, effectively improves the scenario generalization capabilities of vision-based roadside 3D object detectors. The code is available here (https://github.com/yanglei18/SGV3D).

A global and feature-based hybrid algorithm, which integrates global information and feature-based simultaneous localization and mapping (GF-SLAM). This system can operate adaptively when external signals are unstable, thereby avoiding cumulative errors produced by local methods. In agricultural planting bases with abundant circular arc features, the focus is on efficiently exploring the correlations between these features to optimize the robust real-time positioning system for work vehicles. In this process, feature-based SLAM (F-SLAM) is applied to partial positioning using a particle filter. Available global information is then fused using an extended Kalman filter (EKF) for precise positioning and deviation correction in the mapping process, thereby achieving an effective combination of two positioning modes. The proposed model was evaluated using two simulation environments and a comparison with representative techniques. Results showed that GF-SLAM was competitive in normal conditions while requiring fewer computations and significantly reducing the drift in F-SLAM for stable global signals. Switching between these two algorithms eliminated positioning errors to within 1 cm for a test case in which global localization was lost in 54.7% of the route, producing an error within 3 cm. The code will be open source Note to Practitioners —Our adaptive fusion strategy aims to address challenges in real-world agricultural scenarios where global information may be lacking or unstable. This approach enhances the robustness and reliability of robot positioning in practical applications. We invite practitioners to consider the adaptability of our system to diverse environments, particularly those with limited or fluctuating global information. In this paper, we first introduce the EKF module for global localization, then illustrate the establishment of feature maps and particle filter positioning in F-SLAM. We conduct comparison experiments in two virtual environments and real agricultural planting bases, where approximately half of the route lacks global information. The results demonstrate the benefits of our adaptive fusion strategy in practical positioning applications. We also plan to explore the integration of additional sensors, such as cameras, combined with deep learning, to further improve the efficiency and quality of feature extraction. This extension is aimed at mitigating the issue of positioning failure caused by crop and equipment occlusion in agricultural scenarios.

The design of the high-speed on/off valve is challenging due to the interrelated structural parameters of its driving actuator. Hence, this study proposes a multi-objective optimization approach that integrates a backpropagation neural network and artificial fish swarm algorithm optimization techniques to accurately model the electromagnetic solenoid structure. The backpropagation neural network is fitted and trained using simulation data to obtain a reduced-order model of the system, enabling the precise prediction of the system’s output based on the input structural parameters. By employing the artificial fish swarm algorithms, with optimization objectives focusing on the valve’s opening and closing times, a Pareto optimal solution set comprising 30 solutions is generated. Utilizing the optimized structural parameters, a prototype is manufactured and an experimental setup is constructed to verify the dynamic characteristics and flow pressure drop. The high-speed on/off valve achieves an approximate opening and closing time of 3 ms. Notably, the system output predicted using the backpropagation neural network (BPNN) exhibits consistency with the experimental findings, providing a reliable alternative to mathematical modeling.

A multiparameter control strategy and method for the cutting arm of a roadheader is proposed through the operation analysis of roadheader. The method can address the problems of low intelligence and low cutting efficiency faced by the roadheader in the cutting process. The control strategy is divided into two parts: the cutting load identification part and the swing speed control part. The former part is designed using a backpropagation neural network that is optimized by an improved particle swarm optimization algorithm. The latter part is optimally designed using a fuzzy PID controller with improved simulated annealing particle swarm optimization. The simulation analysis in SIMULINK showed that the response time was reduced, proving the robustness of the method. In addition, experimental studies verified the good control effect of the method under different cutting states. The proposed method uses multiparameter to intelligently change the swing speed, providing a theoretical and practical basis for the realization of intelligent and unmanned cutting of roadheader.

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.