A new explosion-proof walking system was designed for the coal mine rescue robot(CMRR) by optimizing the mechanical structure and control algorithm. The mechanical structure innovation lies mainly in the dual-motor dr...A new explosion-proof walking system was designed for the coal mine rescue robot(CMRR) by optimizing the mechanical structure and control algorithm. The mechanical structure innovation lies mainly in the dual-motor drive tracked unit used, which showed high dynamic performance compared with the conventional tracked unit. The control algorithm, developed based on decision trees and neural networking, facilitates autonomous switching between "Velocity-driven Mode" and "Torquedriven Mode". To verify the feasibility and effectiveness of the control strategy, we built a self-designed test platform and used it to debug the control program; we then made a robot prototype and conducted further experiments on single-step, ramp, and rubble terrains. The results show that the proposed walking system has excellent dynamic performance and the control strategy is very efficient, suggesting that a robot with this type of explosion-proof walking system can be successfully applied in Chinese coal mines.展开更多
为了解决现有的目标跟踪算法在煤矿复杂环境下存在精度低和实时性差的问题,基于Tracking by Detection(TBD)范式,提出了YOLO-FasterNet+ByteTrack的煤矿人员跟踪算法。首先,构建FasterNet-Block特征提取模块改进YOLOv7的Backbone,提升...为了解决现有的目标跟踪算法在煤矿复杂环境下存在精度低和实时性差的问题,基于Tracking by Detection(TBD)范式,提出了YOLO-FasterNet+ByteTrack的煤矿人员跟踪算法。首先,构建FasterNet-Block特征提取模块改进YOLOv7的Backbone,提升目标检测阶段的实时性;然后,通过在Neck中引入CBAM注意力机制,提升模型在复杂场景下的特征感知能力;接着,在目标检测的解码阶段引入Soft-NMS,优化模型在人员交叠场景下的检测精度;最后,在目标跟踪阶段,针对人员重叠和遮挡导致的目标ID翻转问题,设计了一种融合GRU和卡尔曼滤波的多目标运动特征预测机制,有效提升了煤矿人员跟踪的准确性。实验结果表明:YOLOFasterNet在煤矿人员数据集上相对于YOLOv7的平均精度提高了3.6%,检测速度提升了8.2FPS;在自定义跟踪数据集GBMOT上,所提目标跟踪算法相对于ByteTrack,MOTA值提升了1.7%,IDSW减少了149次。展开更多
Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this stud...Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.展开更多
A study is presented on the dynamic analysis of a tracked vehicle for mining on the deep seabed of very soft soil. Equations for the interaction between the track and extremely soft seabed are employed to develop a tr...A study is presented on the dynamic analysis of a tracked vehicle for mining on the deep seabed of very soft soil. Equations for the interaction between the track and extremely soft seabed are employed to develop a track/soil interaction module called TVAS. The vehicle is modeled as a multibody dynamic system by the use of a multibody dynamic analysis program. The module developed is cooperated with the multibody dynamic analysis program with a user-defined subroutine. The dynamic behavior and the conceptual design of the mining vehicle on the deep seabed are investigated.展开更多
The prediction of helicopter′s track is very important for Anti-Helicopter Mine System (AHMS) in the battle. However, it is very difficult to get an accurate and reliable prediction when the data is very limited. Thi...The prediction of helicopter′s track is very important for Anti-Helicopter Mine System (AHMS) in the battle. However, it is very difficult to get an accurate and reliable prediction when the data is very limited. This paper tries to establish a track forecast model based on the grey system theory to predict the radial distance, the azimuth and the elevation of a helicopter real-timely. The forecast model of grey system can directly predict the helicopter′s track, with out the need of coordinate conversion (polar coordinate to rectangular coordinate). So the measurement noise is relatively independent, the prediction accuracy can be improved. In the period of sampling, GM model has been established on line to improve prediction precision. The results of simulation indicate that higher prediction precision can be obtained with fewer surveying data.展开更多
基金Project(2012AA041504)supported by the National High-Tech Research and Development Program of ChinaProject(KYLX15_1418)supported by the 2015 Annual General University Graduate Research and Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
文摘A new explosion-proof walking system was designed for the coal mine rescue robot(CMRR) by optimizing the mechanical structure and control algorithm. The mechanical structure innovation lies mainly in the dual-motor drive tracked unit used, which showed high dynamic performance compared with the conventional tracked unit. The control algorithm, developed based on decision trees and neural networking, facilitates autonomous switching between "Velocity-driven Mode" and "Torquedriven Mode". To verify the feasibility and effectiveness of the control strategy, we built a self-designed test platform and used it to debug the control program; we then made a robot prototype and conducted further experiments on single-step, ramp, and rubble terrains. The results show that the proposed walking system has excellent dynamic performance and the control strategy is very efficient, suggesting that a robot with this type of explosion-proof walking system can be successfully applied in Chinese coal mines.
文摘为了解决现有的目标跟踪算法在煤矿复杂环境下存在精度低和实时性差的问题,基于Tracking by Detection(TBD)范式,提出了YOLO-FasterNet+ByteTrack的煤矿人员跟踪算法。首先,构建FasterNet-Block特征提取模块改进YOLOv7的Backbone,提升目标检测阶段的实时性;然后,通过在Neck中引入CBAM注意力机制,提升模型在复杂场景下的特征感知能力;接着,在目标检测的解码阶段引入Soft-NMS,优化模型在人员交叠场景下的检测精度;最后,在目标跟踪阶段,针对人员重叠和遮挡导致的目标ID翻转问题,设计了一种融合GRU和卡尔曼滤波的多目标运动特征预测机制,有效提升了煤矿人员跟踪的准确性。实验结果表明:YOLOFasterNet在煤矿人员数据集上相对于YOLOv7的平均精度提高了3.6%,检测速度提升了8.2FPS;在自定义跟踪数据集GBMOT上,所提目标跟踪算法相对于ByteTrack,MOTA值提升了1.7%,IDSW减少了149次。
文摘Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.
文摘A study is presented on the dynamic analysis of a tracked vehicle for mining on the deep seabed of very soft soil. Equations for the interaction between the track and extremely soft seabed are employed to develop a track/soil interaction module called TVAS. The vehicle is modeled as a multibody dynamic system by the use of a multibody dynamic analysis program. The module developed is cooperated with the multibody dynamic analysis program with a user-defined subroutine. The dynamic behavior and the conceptual design of the mining vehicle on the deep seabed are investigated.
文摘The prediction of helicopter′s track is very important for Anti-Helicopter Mine System (AHMS) in the battle. However, it is very difficult to get an accurate and reliable prediction when the data is very limited. This paper tries to establish a track forecast model based on the grey system theory to predict the radial distance, the azimuth and the elevation of a helicopter real-timely. The forecast model of grey system can directly predict the helicopter′s track, with out the need of coordinate conversion (polar coordinate to rectangular coordinate). So the measurement noise is relatively independent, the prediction accuracy can be improved. In the period of sampling, GM model has been established on line to improve prediction precision. The results of simulation indicate that higher prediction precision can be obtained with fewer surveying data.
