Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

Zhang, Jiaming; Dai, Xiaoyan

doi:10.3390/electronics11142270

Cited by 3 publications

(3 citation statements)

References 26 publications

(54 reference statements)

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“…Robotic grasping in cluttered environments is a challenging problem that has garnered significant attention in the robotics community. Researchers have explored various approaches to tackle this problem, employing both analytical and learning-based techniques [13][14][15]. The attention mechanism efficiently extracts high-quality elements from extensive information with limited resources [16], first applied to image classification by Mnih et al [17].…”

Section: Attention Mechanism and Deep Q Networkmentioning

confidence: 99%

Grasping Unstructured Objects with Full Convolutional Network in Clutter

Zhang

2023

Electronics

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Grasping objects in cluttered environments remains a significant challenge in robotics, particularly when dealing with novel objects that have not been previously encountered. This paper proposes a novel approach to address the problem of robustly learning object grasping in cluttered scenes, focusing on scenarios where the objects are unstructured and randomly placed. We present a unique Deep Q-learning (DQN) framework combined with a full convolutional network suitable for the end-to-end grasping of multiple adhesive objects in a cluttered environment. Our method combines the depth information of objects with reinforcement learning to obtain an adaptive grasping strategy to enable a robot to learn and generalize grasping skills for novel objects in the real world. The experimental results demonstrate that our method significantly improves the grasping performance on novel objects compared to conventional grasping techniques. Our system demonstrates remarkable adaptability and robustness in cluttered scenes, effectively grasping a diverse array of objects that were previously unseen. This research contributes to the advancement of robotics with potential applications, including, but not limited to, redundant manipulators, dual-arm robots, continuum robots, and soft robots.

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Section: Attention Mechanism and Deep Q Networkmentioning

confidence: 99%

Grasping Unstructured Objects with Full Convolutional Network in Clutter

Zhang

2023

Electronics

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“…Robotic grasping in cluttered environments is a challenging problem that has garnered significant attention in the robotics community. In order to tackle this issue, researchers have investigated a number of strategies using both analytical and learning-based methods [7][8][9]. These methods for graspable objects are time consuming and impractical for real-time implementation.…”

Section: Related Workmentioning

confidence: 99%

Research on Perception and Control Technology for Dexterous Robot Operation

Zhang

2023

Electronics

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Robotic grasping in cluttered environments is a fundamental and challenging task in robotics research. The ability to autonomously grasp objects in cluttered scenes is crucial for robots to perform complex tasks in real-world scenarios. Conventional grasping is based on the known object model in a structured environment, but the adaptability of unknown objects and complicated situations is constrained. In this paper, we present a robotic grasp architecture of attention-based deep reinforcement learning. To prevent the loss of local information, the prominent characteristics of input images are automatically extracted using a full convolutional network. In contrast to previous model-based and data-driven methods, the reward is remodeled in an effort to address the sparse rewards. The experimental results show that our method can double the learning speed in grasping a series of randomly placed objects. In real-word experiments, the grasping success rate of the robot platform reaches 90.4%, which outperforms several baselines.

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“…The methods can be divided into backstepping, feedback domain and LMI (Linear Matrix Inequalities) methods. In [ 20 ], the authors developed a backstepping sampled data control method for a flexible robotic manipulator whose internal dynamic is completely unknown. In [ 21 ], the authors proposed a feedback domain method for sampled-data nonlinear systems.…”

Section: Related Workmentioning

confidence: 99%

Switching Adaptive Control with Applications on Robot Manipulators

Wang

Zheng

Deng

et al. 2022

Entropy

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This paper concentrates on the study of logic-based switching adaptive control. Two different cases will be considered. In the first case, the finite time stabilization problem for a class of nonlinear system is studied. Based on the recently developed adding a barrier power integrator technique, a new logic-based switching adaptive control method is proposed. In contrast with the existing results, finite time stability can be achieved when the considered systems contain both fully unknown nonlinearties and unknown control direction. Moreover, the proposed controller has a very simple structure and no approximation methods, e.g., neural networks/fuzzy logic, are needed. In the second case, the sampled-data control for a class of nonlinear system is investigated. New sampled-data logic-based switching mechanism is proposed. Compared with previous works, the considered nonlinear system has an uncertain linear growth rate. The control parameters and the sampling time can be adjusted adaptively to render the exponential stability of the closed loop system. Applications in robot manipulators are conducted to verify the proposed results.

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Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

Cited by 3 publications

References 26 publications

Grasping Unstructured Objects with Full Convolutional Network in Clutter

Grasping Unstructured Objects with Full Convolutional Network in Clutter

Research on Perception and Control Technology for Dexterous Robot Operation

Switching Adaptive Control with Applications on Robot Manipulators

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