Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach ...Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.展开更多
Robot teleoperation plays an important role in industrial manufacturing in unknown and dangerous environments beyond human reach.In telerobotic manufacturing tasks,environmental interaction forces may vary significant...Robot teleoperation plays an important role in industrial manufacturing in unknown and dangerous environments beyond human reach.In telerobotic manufacturing tasks,environmental interaction forces may vary significantly from task to task.Therefore,it is crucial to provide operators with the specific proportional feedback of environmental interaction forces to enhance their environmental awareness and manipulation capabilities.However,variable time delays and various scales of environmental interaction force feedback seriously affect the system stability,which should be rigorously addressed when designing control parameters.To cope with these difficulties,a position and scaled force tracking control framework is proposed and the LyapunovKrasovskii theory is used to obtain a simple algebraic stability criterion with the scaling factor of the environmental interaction force feedback.In addition,a low-pass filter-based radial basis function neural network is designed to avoid the effect of the measurement noise and the sudden change of the non-passive environmental interaction force on the system stability.Compared with different controllers in various telerobotic manufacturing tasks such as heavy lifting,cutting,and polishing,our proposed method achieves better position and scaled force tracking performance.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos.61806028,61672437 and 61702428Sichuan Sci-ence and Technology Program under Grant Nos.2018GZ0245,21ZDYF2484,18ZDYF3269,2021YFN0104,2021YFN0104,21GJHZ0061,21ZDYF3629,2021YFG0295,2021YFG0133,21ZDYF2907,21ZDYF0418,21YYJC1827,21ZDYF3537,21ZDYF3598,2019YJ0356the Chinese Scholarship Council under Grant Nos.202008510036,201908515022。
文摘Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.
基金supported by the National Natural Science Foundation of China(Grant Nos.52188102,52105515,62373161)。
文摘Robot teleoperation plays an important role in industrial manufacturing in unknown and dangerous environments beyond human reach.In telerobotic manufacturing tasks,environmental interaction forces may vary significantly from task to task.Therefore,it is crucial to provide operators with the specific proportional feedback of environmental interaction forces to enhance their environmental awareness and manipulation capabilities.However,variable time delays and various scales of environmental interaction force feedback seriously affect the system stability,which should be rigorously addressed when designing control parameters.To cope with these difficulties,a position and scaled force tracking control framework is proposed and the LyapunovKrasovskii theory is used to obtain a simple algebraic stability criterion with the scaling factor of the environmental interaction force feedback.In addition,a low-pass filter-based radial basis function neural network is designed to avoid the effect of the measurement noise and the sudden change of the non-passive environmental interaction force on the system stability.Compared with different controllers in various telerobotic manufacturing tasks such as heavy lifting,cutting,and polishing,our proposed method achieves better position and scaled force tracking performance.
