This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a...This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a pair of pectoral fins,a wire-driven active body covered with soft skin,and a compliant tail.The CPG model consists of four input parameters:the flapping amplitude,the flapping angular velocity,the flapping offset,and the time ratio between the beat phase and the restore phase in flapping.The robot fish is equipped with three infrared sensors mounted on the left,front and right of the robot fish,as well as an inertial measurement unit,from which the surrounding obstacles and moving direction can be sensed.Based on these sensor signals,the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions.Four sets of experiments are presented,including avoiding a static obstacle,avoiding a moving obstacle,tracking a designated direction and tracking a designated direction with an obstacle in the path.The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively.展开更多
In this paper,we present a novel particle filter(PF)-based direct position tracking method utilizing multiple distributed observation stations.Traditional passive tracking methods are anchored on repetitive position e...In this paper,we present a novel particle filter(PF)-based direct position tracking method utilizing multiple distributed observation stations.Traditional passive tracking methods are anchored on repetitive position estimation,where the set of consecutive estimates provides the tracking trajectory,such as Two-step and direct position determination methods.However,duplicate estimates can be computationally expensive.In addition,these techniques suffer from data association problems.The PF algorithm is a tracking method that avoids these drawbacks,but the conventional PF algorithm is unable to construct a likelihood function from the received signals of multiple observatories to determine the weights of particles.Therefore,we developed an improved PF algorithm with the likelihood function modified by the projection approximation subspace tracking with deflation(PASTd)algorithm.The proposed algorithm uses the projection subspace and spectral function to replace the likelihood function of PF.Then,the weights of particles are calculated jointly by multiple likelihood functions.Finally,the tracking problem of multiple targets is solved by multiple sets of particles.Simulations demonstrate the effectiveness of the proposed method in terms of computational complexity and tracking accuracy.展开更多
This study investigated the effect of transcranial direct current stimulation（t DCS） polarity depending on lateralized function of task property in normal individuals performing visuomotor and simple repetitive task...This study investigated the effect of transcranial direct current stimulation（t DCS） polarity depending on lateralized function of task property in normal individuals performing visuomotor and simple repetitive tasks. Thirty healthy participants with no neurological disorders were recruited to participate in this study. Participants were randomly allocated into active or control condition. For the active condition, t DCS intensity was 2 m A with stimulation applied for 15 minutes to the right hemisphere（t DCS condition）. For the sham control, electrodes were placed in the same position, but the stimulator was turned off after 30 seconds（sham condition）. The tapping and tracking task tests were performed before and after for both conditions. Univariate analysis revealed significant difference only in the tracking task. For direct comparison of both tasks within each group, the tracking task had significantly higher Z score than the tapping task in the t DCS group（P 〈 0.05）. Thus, our study indicates that stimulation of the right hemisphere using t DCS can effectively improve visuomotor（tracking） task over simple repetitive（tapping） task.展开更多
This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small drones.The key contributions of our system include:1)t...This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small drones.The key contributions of our system include:1)the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation;2)adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation;and 3)an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization.A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system:LSD-SLAM.For the relocalization task,our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(class A)(Grant No.XDA22040203)the Fundamental Research Funds for the Central Universities(Grant No.2019XX01)+1 种基金GDNRC[2020]031the Natural Science Foundation of Guangdong Province(Grant No.2020A1515010621).
文摘This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a pair of pectoral fins,a wire-driven active body covered with soft skin,and a compliant tail.The CPG model consists of four input parameters:the flapping amplitude,the flapping angular velocity,the flapping offset,and the time ratio between the beat phase and the restore phase in flapping.The robot fish is equipped with three infrared sensors mounted on the left,front and right of the robot fish,as well as an inertial measurement unit,from which the surrounding obstacles and moving direction can be sensed.Based on these sensor signals,the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions.Four sets of experiments are presented,including avoiding a static obstacle,avoiding a moving obstacle,tracking a designated direction and tracking a designated direction with an obstacle in the path.The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively.
基金supported by China NSF Grants(62371225,62371227)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX250590).
文摘In this paper,we present a novel particle filter(PF)-based direct position tracking method utilizing multiple distributed observation stations.Traditional passive tracking methods are anchored on repetitive position estimation,where the set of consecutive estimates provides the tracking trajectory,such as Two-step and direct position determination methods.However,duplicate estimates can be computationally expensive.In addition,these techniques suffer from data association problems.The PF algorithm is a tracking method that avoids these drawbacks,but the conventional PF algorithm is unable to construct a likelihood function from the received signals of multiple observatories to determine the weights of particles.Therefore,we developed an improved PF algorithm with the likelihood function modified by the projection approximation subspace tracking with deflation(PASTd)algorithm.The proposed algorithm uses the projection subspace and spectral function to replace the likelihood function of PF.Then,the weights of particles are calculated jointly by multiple likelihood functions.Finally,the tracking problem of multiple targets is solved by multiple sets of particles.Simulations demonstrate the effectiveness of the proposed method in terms of computational complexity and tracking accuracy.
文摘This study investigated the effect of transcranial direct current stimulation（t DCS） polarity depending on lateralized function of task property in normal individuals performing visuomotor and simple repetitive tasks. Thirty healthy participants with no neurological disorders were recruited to participate in this study. Participants were randomly allocated into active or control condition. For the active condition, t DCS intensity was 2 m A with stimulation applied for 15 minutes to the right hemisphere（t DCS condition）. For the sham control, electrodes were placed in the same position, but the stimulator was turned off after 30 seconds（sham condition）. The tapping and tracking task tests were performed before and after for both conditions. Univariate analysis revealed significant difference only in the tracking task. For direct comparison of both tasks within each group, the tracking task had significantly higher Z score than the tapping task in the t DCS group（P 〈 0.05）. Thus, our study indicates that stimulation of the right hemisphere using t DCS can effectively improve visuomotor（tracking） task over simple repetitive（tapping） task.
基金funded by the National Natural Science Foundation of China(Grant No.61502188).
文摘This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small drones.The key contributions of our system include:1)the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation;2)adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation;and 3)an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization.A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system:LSD-SLAM.For the relocalization task,our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.
