RGB-D sensor-based visual target detection and tracking for an intelligent wheelchair robot in indoors environments

Xiao, Hanzhen; Li, Zhijun; Yang, Chenguang; Wang, Yüan; Wang, Liyang

doi:10.1007/s12555-014-0353-4

Cited by 25 publications

(9 citation statements)

References 16 publications

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“…From the performance measures given in Fig. 5, we can see that using the proposed prey-predator model the pursuer was able to keep appropriate distance to the target fairly well, even under the situation with perception uncertainty 5 . When the pursuer was equipped with simple inference method or ESN to predict the position of the target, the pursuer is able to keep the miss hit rate as low as 1%.…”

Section: B Target Performs Random Exploration In a Confined Environmentmentioning

confidence: 91%

“…Similarly, a Kalman filter with a second-order motion model is implemented to predict the state of the target and select candidate patches for their compressive tracking system [4]. In the tracking strategy for an intelligent wheelchair robot in indoor environments [5], an Extended Kalman filter is proposed to estimate robot pose based on Haar-like features, followed by obstacle avoidance navigation approach based on spline trajectory planning and optimization. An Extended Kalman filter based vision feedback controller to compensate for the positioning error is implemented for visual object tracking by autonomous vehicles in [6].…”

Section: Introductionmentioning

confidence: 99%

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A prey-predator model for efficient robot tracking

Tang

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Tracking is a common topic in various areas of robotics research. Motivated by the hunting behavior of predators in nature, we propose a prey-predator model for efficient robot tracking. The head direction and speed of the pursuer is automatically adjusted according to the position and velocity of the prey. Under the situation with perception uncertainty, where the actual location of the prey is not observable, the pursuer predicts the location of the prey according to simple inference, an online adaptive autoregressive model, or an online adaptive echo state network. Simulation results demonstrate that the proposed prey-predator model is able to control the pursuer and to track the prey efficiently, even under perception uncertainty. Simple inference gives better results when the motion of the target is piecewise linear, while echo state network is more suitable when the dynamics of the target are more complex. The proposed prey-predator model thus provides an efficient method tracking targets with various statistical nature of trajectories for applications such as underwater robot tracking, human tracking and team formation.

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Section: B Target Performs Random Exploration In a Confined Environmentmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

A prey-predator model for efficient robot tracking

Tang

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…In traditional visual servoing, features are first extracted from the image, and then a controller is designed to reduce the position error between the desired and current vectors of features to zero [36]. Traditional visual servoing relies on feature extraction.…”

Section: Control Strategy Based On Visual Feedbackmentioning

confidence: 99%

Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Qiu

et al. 2017

IEEE Trans. Fuzzy Syst.

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This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface (BMI) and vision feedback. Vision compressive sensing, brainmachine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by video captured by a camera, allowing him/her to visualize the manipulator's workspace and the movements being executed. Then, compressed images are used as feedback errors in a nonvector space for producing SSVEP (Steady-State Visual Evoked Potentials) electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed following Lyapunov synthesis to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.

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“…Being attached on the robot arm, eye-in-hand VS provides a narrower field of view as compared to eye-tohand servoing. Many control schemes use either a direct visual servoing or a dual loop system [19], [31]. A visual processing method for human tracking using intelligent wheelchair was developed in [19], and a visual servoing for stabilization of an nonholonomic mobile robot was proposed by incorporating a model predictive control [31].…”

Section: Introductionmentioning

confidence: 99%

Development of Sensory-Motor Fusion-Based Manipulation and Grasping Control for a Robotic Hand-Eye System

et al. 2016

IEEE Trans. Syst. Man Cybern, Syst.

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In the paper, sensory-motor fusion based manipulation and grasping control has been developed for a robotic hand-eye system. The proposed hierarchical control architecture has three modules: vision servoing, surface electromyography (sEMG) based movement recognition, and hybrid force and motion optimization for manipulation and grasping. A stereo camera is used to obtain the 3D point cloud of a target object and provides the desired operational position. The AdaBoostbased motion recognition is employed to discriminate different movements based on sEMG of human upper limbs. The operational space motion planning for bionic arm and force planning for multi-fingered robotic hand can be both transformed as a convex optimization problem with various constraints. A neural dynamics optimization solution is proposed and implemented online. The proposed formulation can achieve a substantial reduction of computational load. The actual implementation includes a bionic arm with dextrous hand, high-speed active vision, and a EMG sensors. A series of manipulation tasks consisting of tracking/recogniting/grasping/ of an object are implemented and tested, and experiment results exhibit the responsiveness and flexibility of the proposed sensory motion fusion approach.

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RGB-D sensor-based visual target detection and tracking for an intelligent wheelchair robot in indoors environments

Cited by 25 publications

References 16 publications

A prey-predator model for efficient robot tracking

A prey-predator model for efficient robot tracking

Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Development of Sensory-Motor Fusion-Based Manipulation and Grasping Control for a Robotic Hand-Eye System

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