Teleoperation of humanoid baxter robot using haptic feedback

Ju, Zhaojie; Yang, Chenguang; Li, Zhijun; Cheng, Long; Ma, Hongbin

doi:10.1109/mfi.2014.6997721

Cited by 57 publications

(27 citation statements)

References 22 publications

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“…The point cloud of the end-effector points of both input device and manipulator is created based on Monte Carlo method. The matching process follows our previous work in [22]:…”

Section: B Workpace Matchingmentioning

confidence: 99%

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Yang

Wang

Cheng

et al. 2017

IEEE Trans. Cybern.

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281

151

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In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

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“…The point cloud of the end-effector points of both input device and manipulator is created based on Monte Carlo method. The matching process follows our previous work in [22]:…”

Section: B Workpace Matchingmentioning

confidence: 99%

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Yang

Wang

Cheng

et al. 2017

IEEE Trans. Cybern.

Self Cite

281

151

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show abstract

“…Recent advancements in robotics and automated systems have led to the expansion of autonomous capabilities and more intelligent machines being utilised in ever more varied applications [1,2]. The capability of adapting to changing environments is a necessary skill for task generalised robots [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…However, direct mapping of results from a kinematic model to the robot joint controller is inherently open-loop and is identified to cause task space drifts. Therefore, they have, in addition, recommended the use of closed loop control algorithms to address these drifts [2].…”

Section: Introductionmentioning

confidence: 99%

“…According to Ju et al [2], unstructured environments remain a large challenge for intelligent robots that would require a complex analytical approach to form the solution. While deriving of models requires a great deal of data and knowledge of the physical parameters relating to the robotic task, use of more dynamic robotic actuators make it nearly impossible to model the physics, thus they conclude that manual teaching is an efficient but exhaustive approach [2]. In such cases, empirical methods will provide an increased cognitive and adaptive capability to the robots, while reducing or completely removing the need to manually model a robotic solution [3].…”

Section: Introductionmentioning

confidence: 99%

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Review of Deep Learning Methods in Robotic Grasp Detection

Caldera

Rassau

Chai

2018

MTI

172

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For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

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“…In Liu, Zhang, Fu, and Yang (2013), the welder-related work is caught by the Leap Motion sensor, which is estimated by a soldering robot with teleoperation networks. Moreover, the wearable devices, for example, exoskeleton (Jo, Park, & Bae, 2013) or joystick, or Omni haptic device (Ju, Yang, Li, Cheng, & Ma, 2014) are normally used. In this paper, we investigate the wearable device, MYO Armband together with the motion capture system using a Kinect sensor to teleoperate a Baxter robot optimized by Kalman filtering (KF) based sensor fusion.…”

Section: Introductionmentioning

confidence: 99%

Teleoperation control of Baxter robot using Kalman filter-based sensor fusion

Yang

Wan

et al. 2017

Systems Science & Control Engineering

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Kalman filter has been successfully applied to fuse the motion capture data collected from Kinect sensor and a pair of MYO armbands to teleoperate a robot. A new strategy utilizing the vector approach has been developed to accomplish a specific motion capture task. The arm motion of the operator is captured by a Kinect sensor and programmed with Processing software. Two MYO armbands with the inertial measurement unit embedded are worn on the operator's arm, which is used to detect the upper arm motion of the human operator. This is utilized to recognize and to calculate the precise speed of the physical motion of the operator's arm. User Datagram Protocol is employed to send the human movement to a simulated Baxter robot arm for teleoperation. In order to obtain joint angles for human limb utilizing vector approach, RosPy and Python script programming has been utilized. A series of experiments have been conducted to test the performance of the proposed technique, which provides the basis for the teleoperation of simulated Baxter robot. ARTICLE HISTORY

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Teleoperation of humanoid baxter robot using haptic feedback

Cited by 57 publications

References 22 publications

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Review of Deep Learning Methods in Robotic Grasp Detection

Teleoperation control of Baxter robot using Kalman filter-based sensor fusion

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