Design of Sensing System and Anticipative Behavior for Human Following of Mobile Robots

Hu, Jwu‐Sheng; Wang, Jyun-Ji; Ho, Daniel M.

doi:10.1109/tie.2013.2262758

Cited by 89 publications

(47 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several human detection and tracking systems based on RGB-D sensing technology have also been extensively investigated. Hu et al 24 developed a human tracking system based on a novel 3D meanshift algorithm on a RGB-D camera for mobile robots. The depth information was added into the target model and the candidate model as a kernel weighting function to predict the human trajectory and to take preemptive action.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

People detection and tracking using RGB-D cameras for mobile robots

Liu

Luo

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

People detection and tracking is an essential capability for mobile robots in order to achieve natural human-robot interaction. In this article, a human detection and tracking system is designed and validated for mobile robots using color data with depth information RGB-depth (RGB-D) cameras. The whole framework is composed of human detection, tracking and re-identification. Firstly, ground points and ceiling planes are removed to reduce computation effort. A prior-knowledge guided random sample consensus fitting algorithm is used to detect the ground plane and ceiling points. All left points are projected onto the ground plane and subclusters are segmented for candidate detection. Meanshift clustering with an Epanechnikov kernel is conducted to partition different points into subclusters. We propose the new idea of spatial region of interest plan view maps which are employed to identify human candidates from point cloud subclusters. Here, a depthweighted histogram is extracted online to feature a human candidate. Then, a particle filter algorithm is adopted to track the human's motion. The integration of the depth-weighted histogram and particle filter provides a precise tool to track the motion of human objects. Finally, data association is set up to re-identify humans who are tracked. Extensive experiments are conducted to demonstrate the effectiveness and robustness of our human detection and tracking system.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The depth information is added into the target model and target candidate as a kernel weighting function. The modified probability of the feature u ¼ 1; 2; :::; m in the target model 24 is then computed as in equation (5) …”

Section: Motion Trackingmentioning

confidence: 99%

People detection and tracking using RGB-D cameras for mobile robots

Liu

Luo

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Neuromotor experimental studies have shown that the impedance of the human joints can be voluntarily adapted during motion. If the delicate impedance adaptation skills of human operator can be incorporated into robot control, it would greatly benefit the physical human-robot interaction [46]. It is well known that the limb endpoint visco-elastic properties can be regulated by the relevant muscles and the pose configuration in different ways.…”

Section: Bio-inspired Control Designmentioning

confidence: 99%

Guest Editorial An Overview of Biomedical Robotics and Bio-Mechatronics Systems and Applications

Yang

Burdet

2016

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

“…Wheeled mobile robots are playing an important role in social application [1]. Their dynamics under nonholonomic constraints can be formulated into a chained form.…”

Section: Introductionmentioning

confidence: 99%

Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

Xiao

Yang

et al. 2017

IEEE Trans. Ind. Electron.

130

View full text Add to dashboard Cite

In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input u1 an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization can be formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach.Index Terms-Robust nonholonomic mobile robots, Scaling transformation, Model predictive control(MPC), Primal-dual neural network (PDNN).

show abstract

Design of Sensing System and Anticipative Behavior for Human Following of Mobile Robots

Cited by 89 publications

References 38 publications

People detection and tracking using RGB-D cameras for mobile robots

People detection and tracking using RGB-D cameras for mobile robots

Guest Editorial An Overview of Biomedical Robotics and Bio-Mechatronics Systems and Applications

Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

Contact Info

Product

Resources

About