Kinematics modeling and experimental verification of baxter robot

Ju, Zhaojie; Yang, Chenguang; Ma, Hongbin

doi:10.1109/chicc.2014.6896430

Cited by 67 publications

(38 citation statements)

References 9 publications

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“…The matrices j−1 A j are the homogeneous transform matrices between consecutive links [25]. Based on the segmented point cloud, we could easily generate a 3-D model of the robot in realtime [26], i.e., the red 3-D model consisting of spheres in Fig.…”

Section: Identification Of Collision Pointsmentioning

confidence: 99%

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Yang

Wang

Cheng

et al. 2017

IEEE Trans. Cybern.

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277

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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Section: Identification Of Collision Pointsmentioning

confidence: 99%

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Yang

Wang

Cheng

et al. 2017

IEEE Trans. Cybern.

Self Cite

277

151

View full text Add to dashboard Cite

show abstract

“…20 In this paper, we use the D-H parameter method to model the kinematics of the NAO robot's arm. The NAO robot arm has five degrees of freedom.…”

Section: Kinematic Modeling For Left Arm Of Naomentioning

confidence: 99%

“…Denavit-Hartenberg (D-H) notations are widely used to describe the kinematic model of a robot. 20 In this paper, we use the D-H parameter method to model the kinematics of the NAO robot's arm. Figure 3 shows the structure of the NAO arm.…”

Section: Kinematic Modeling For Left Arm Of Naomentioning

confidence: 99%

Multi‐target detection and grasping control for humanoid robot NAO

Zhang

Yang

et al. 2019

Adaptive Control & Signal

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Graspirng objects is an important capability for humanoid robots. Due to complexity of environmental and diversity of objects, it is difficult for the robot to accurately recognize and grasp multiple objects. In response to this problem, we propose a robotic grasping method that uses the deep learning method You Only Look Once v3 for multi-target detection and the auxiliary signs to obtain target location. The method can control the movement of the robot and plan the grasping trajectory based on visual feedback information. It is verified by experiments that this method can make the humanoid robot NAO grasp the object effectively, and the success rate of grasping can reach 80% in the experimental environment.

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“…software [version ROS], where Baxter is an industrial robot with two 7-DOF arms built by Rethink Robotics (Boston, Massachusetts), 24,25 and MoveIt! is the most widely used open-source software for motion planning, manipulation, kinematics, and other advanced robotics applications.…”

Section: Mgb-rrt()mentioning

confidence: 99%

Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

Han

Nie

Chen

et al. 2018

International Journal of Advanced Robotic Systems

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Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing-Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing-Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing-Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

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Kinematics modeling and experimental verification of baxter robot

Cited by 67 publications

References 9 publications

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Multi‐target detection and grasping control for humanoid robot NAO

Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

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