Incremental visual servo control of robotic manipulator for autonomous capture of non-cooperative target

Dong, Gangqi; Zhu, Zheng H.

doi:10.1080/01691864.2016.1229633

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Houshangi (1990) gave interesting results on grasping a moving object with a 6DOF manipulator using vision. In this section the method of motion planning based on separation of the direction and size of velocity in the working space is adopted, using the inverse kinematics algorithm and a principle similar to (Bing & Xiang, 2008), (Dong & Zhu, 2016). The available data from the sensors is the position of the human hand measured with a sampling time Δ .…”

Section: Motion Planningmentioning

confidence: 99%

Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Melchiorre

Scimmi

Mauro

et al. 2018

Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics

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The paper analyses a possible cooperative task between a human operator and a robot. Operator and robot are interfaced by Microsoft Kinect® which is used to detect the position of an upper limb of the operator. The robot is driven by a control algorithm designed to track the hand of the human and to obtain a hand-over in the final part of the trajectory. The paper describes the algorithm and shows its performance with different velocities of the limbs by means of tests carried out in a simulation environment.

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Section: Motion Planningmentioning

confidence: 99%

Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Melchiorre

Scimmi

Mauro

et al. 2018

Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics

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“…Li proposed a trajectory planning method for space station manipulators based on deep reinforcement learning, and combined this with the artificial potential field method to improve the convergence of training [7]. Dong developed a new autonomous incremental visual servo control law for the robotic manipulator to capture a non-cooperative target, where the control input is the incremental joint angle, to avoid multiple solutions in the existing inverse kinematics [8]. Cristian proposed a force control framework based on reinforcement learning on the control of rigid robot manipulators, combining traditional force control methods with the Soft Actor-Critic (SAC) algorithm to avoid damage to the environment during in the process of approaching the environment [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Dual-Arm Control of Lunar Assisted Robot Based on Hierarchical Reinforcement Learning under Unstructured Environment

2022

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When a lunar assisted robot helps an astronaut turn over or transports the astronaut from the ground, the trajectory of the robot’s dual arms should be automatically planned according to the unstructured environment on the lunar surface. In this paper, a dual-arm control strategy model of a lunar assisted robot based on hierarchical reinforcement learning is proposed, and the trajectory planning problem is modeled as a two-layer Markov decision process. In the training process, a reward function design method based on the idea of the artificial potential field method is proposed, and the reward information is fed back in a dense reward method, which significantly reduces the invalid exploration space and improves the learning efficiency. Large-scale tests are carried out in both simulated and physical environments, and the results demonstrate the effectiveness of the method proposed in this paper. This research is of great significance in respect of human–robot interaction, environmental interaction, and intelligent control of robots.

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“…The accuracy of the visual system and the mechanical system also affect the rate of success of operation, operational accuracy, and cost. In the “eye in hand” hand–eye coordination operation system, the camera is fixed at the end of the manipulator, and the target object is close to it to prevent the manipulator from occluding the target object and to achieve a high-resolution image [ 11 ]. The requirements on the accuracies of visual measurement and positioning of the manipulator are low, and the small displacement of the fruit does not affect its grip.…”

Section: Introductionmentioning

confidence: 99%

A “Global–Local” Visual Servo System for Picking Manipulators

Shi

Zhang

et al. 2020

Sensors

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During the process of automated crop picking, the two hand–eye coordination operation systems, namely “eye to hand” and “eye in hand” have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a “global–local” visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global–local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.

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Incremental visual servo control of robotic manipulator for autonomous capture of non-cooperative target

Cited by 9 publications

References 20 publications

Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Research on Dual-Arm Control of Lunar Assisted Robot Based on Hierarchical Reinforcement Learning under Unstructured Environment

A “Global–Local” Visual Servo System for Picking Manipulators

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