The Experts below are selected from a list of 37806 Experts worldwide ranked by ideXlab platform
Alessandro Astolfi - One of the best experts on this subject based on the ideXlab platform.
-
Shared-control for the Kinematic Model of a rear-wheel drive car
2015 American Control Conference (ACC), 2015Co-Authors: Jingjing Jiang, Alessandro AstolfiAbstract:This paper presents a shared-control algorithm for the Kinematic Model of a rear-wheel drive car, for which the set of feasible Cartesian positions is defined by a group of linear inequalities. The shared-control scheme is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the shared-control law.
-
Shared-control for the Kinematic Model of a mobile robot
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Jingjing Jiang, Alessandro AstolfiAbstract:This paper presents a shared-control algorithm for the Kinematic Model of a mobile robot. The set of feasible position of the robot is defined by a group of linear inequalities. The shared-control strategy is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simulation results illustrate the effectiveness of the algorithm.
Yan Qiu Chen - One of the best experts on this subject based on the ideXlab platform.
-
3D tracking swimming fish school with learned Kinematic Model using LSTM network
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Shuo Hong Wang, Jingwen Zhao, Zhi-ming Qian, Yan Qiu ChenAbstract:This paper proposes a reliable 3D fish tracking method using a novel master-slave camera setup. Instead of conventional dynamic Models that rely on prior knowledge about target Kinematics, the proposed method learns the Kinematic Model with a Long Short-Term Memory (LSTM) network. On this basis, the 3D state of fish at each moment is predicted by LSTM network. We propose to use an innovative master-view-tracking-first strategy. The fish are first tracked in the master view. Cross-view association is then established utilizing motion continuity and epipolar constraint cues. Experiments on data sets of different fish densities show that the proposed method is effective and outperforms the state-of-the-art methods.
-
Learning Kinematic Model of targets in videos from fixed cameras
2016 IEEE International Conference on Multimedia and Expo (ICME), 2016Co-Authors: Xi En Cheng, Shuo Hong Wang, Yan Qiu ChenAbstract:Object tracking is a key step of video analysis, while a motion Model is crucial for object tracking. Concerning videos captured with fixed cameras, a sequence of a target's motion data may suggest the target's Kinematic Model with respect to the imaging system. In this paper we Model the target's Kinematic Model by learning a long short-term memory network. This Kinematic Model can serve as a discriminative Model and determine the probability of a sequence of velocities. In order to improve the expressive ability of the Kinematic Model, we partition units of the network into groups and activate groups at different temporal resolutions. With this improvement the Kinematic Model can also describe the abrupt motion of targets. We have conducted experiments to evaluate the performance of the proposed method, using both a fish tracking method and state-of-the-art tracking methods.
-
3D tracking targets via Kinematic Model weighted particle filter
2016 IEEE International Conference on Multimedia and Expo (ICME), 2016Co-Authors: Xi En Cheng, Shuo Hong Wang, Yan Qiu ChenAbstract:Automatically and reliably tracking numerous flying objects in 3D space is of great significance for not only scientific researches such as collective behavior analysis, but also practical applications such as designing multi-agent robots. However, it remains a challenging task due to the large population, similar appearance, and severe occlusion happening in 2D images. This paper proposes a 3D tracking method that is capable of tracking individuals of a swarm of flying objects using the particle filtering technique. Each particle is not only weighted by the observation Model but also weighted by the Kinematic Model. The Kinematic Model is Modeled by learning a long short-term memory network on sequences of velocities. Experimental results show that the Kinematic Model significantly improves the efficiency of estimating target's motion state, and show that the proposed method outperforms the state-of-the-art methods.
-
ICME - Learning Kinematic Model of targets in videos from fixed cameras
2016 IEEE International Conference on Multimedia and Expo (ICME), 2016Co-Authors: Xi En Cheng, Shuo Hong Wang, Yan Qiu ChenAbstract:Object tracking is a key step of video analysis, while a motion Model is crucial for object tracking. Concerning videos captured with fixed cameras, a sequence of a target's motion data may suggest the target's Kinematic Model with respect to the imaging system. In this paper we Model the target's Kinematic Model by learning a long short-term memory network. This Kinematic Model can serve as a discriminative Model and determine the probability of a sequence of velocities. In order to improve the expressive ability of the Kinematic Model, we partition units of the network into groups and activate groups at different temporal resolutions. With this improvement the Kinematic Model can also describe the abrupt motion of targets. We have conducted experiments to evaluate the performance of the proposed method, using both a fish tracking method and state-of-the-art tracking methods.
Gerald F Harris - One of the best experts on this subject based on the ideXlab platform.
-
an upper extremity Kinematic Model for evaluation of hemiparetic stroke
Journal of Biomechanics, 2006Co-Authors: Brooke Hingtgen, John R Mcguire, Mei Wang, Gerald F HarrisAbstract:Quantification of rehabilitation progress is necessary for accurately assessing clinical treatments. A three-dimension (3D) upper extremity (UE) Kinematic Model was developed to obtain joint angles of the trunk, shoulder and elbow using a Vicon motion analysis system. Strict evaluation confirmed the system's accuracy and precision. As an example of application, the Model was used to evaluate the upper extremity movement of eight hemiparetic stroke patients with spasticity, while completing a set of reaching tasks. Main outcome measures include Kinematic variables of movement time, range of motion, peak angular velocity, and percentage of reach where peak velocity occurs. The Model computed motion patterns in the affected and unaffected arms. The unaffected arm showed a larger range of motion and higher angular velocity than the affected arm. Frequency analysis (power spectrum) demonstrated lower frequency content for elbow angle and angular velocity in the affected limb when compared to the unaffected limb. The Model can accurately quantify UE arm motion, which may aid in the assessment and planning of stroke rehabilitation, and help to shorten recovery time.
-
Upper extremity motion assessment in adult ischemic stroke patients: a 3-D Kinematic Model
2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001Co-Authors: J. Van Bogart, J. Mcguire, Gerald F HarrisAbstract:As part of a larger evaluative study of the effects of botulinum toxin type A (BTA) in ischemic stroke patients, a Kinematic Model of the trunk and upper extremities (UE) has been developed. The 3-D Model provides a comprehensive method of assessing UE motion during performance tasks including exercises in reaching, grasping, and releasing. The 17-marker system tracks UE motion at a rate of 120 SPS with 7 infrared CCD cameras. The biomechanical Model developed for the system allows expression of torso, shoulder, elbow, and wrist motion in terms of Euler expressions. Concurrent EMG data is used to confirm periods of co-contraction and spasticity during planned movement. Preliminary trials with the system indicate sufficient fidelity for continued clinical trials.
Jingjing Jiang - One of the best experts on this subject based on the ideXlab platform.
-
Shared-control for the Kinematic Model of a rear-wheel drive car
2015 American Control Conference (ACC), 2015Co-Authors: Jingjing Jiang, Alessandro AstolfiAbstract:This paper presents a shared-control algorithm for the Kinematic Model of a rear-wheel drive car, for which the set of feasible Cartesian positions is defined by a group of linear inequalities. The shared-control scheme is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the shared-control law.
-
Shared-control for the Kinematic Model of a mobile robot
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Jingjing Jiang, Alessandro AstolfiAbstract:This paper presents a shared-control algorithm for the Kinematic Model of a mobile robot. The set of feasible position of the robot is defined by a group of linear inequalities. The shared-control strategy is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simulation results illustrate the effectiveness of the algorithm.
Fumitoshi Matsuno - One of the best experts on this subject based on the ideXlab platform.
-
Experimental study of Redundant Snake Robot Based on Kinematic Model
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007Co-Authors: Motoyasu Tanaka, Fumitoshi MatsunoAbstract:In this paper we consider Modeling and control of a redundant snake robot with wheeled link mechanism based on Kinematic Model. We derive a Kinematic Model of a snake robot with introducing links without wheels and shape controllable points in the snake robot's body in order to make the system redundancy controllable. By using redundancy, it becomes possible to accomplish the main objective of controlling the position and the attitude of the snake robot head and the shape of the snake robot, and the sub-objective of the singular configuration avoidance. Simulations and experimental results show the validity of the control law and how snake robots move at the neighborhood of the singular configuration.
