Crack localization and segmentation are essential for infrastructure maintenance and safety assessments, enabling timely repairs and preventing structural failures. Despite advancements in deep learning, crack segment...
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The literature of Deep Active Inference, implementing the generative, biologically inspired Active Inference framework with the Deep Learning approach, often makes use of a hidden state transition model to generate cu...
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Autonomous navigation is critical for autonomous underwater vehicle to complete underwater tasks, and attitude is an essential parameter of autonomous navigation. Inspired by the biomimicry studies, three attitude est...
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This study explores the field of Intelligent Control Systems (ICS) and how they have revolutionized robotics and industrial automation. The benefits of ICS over conventional PID control techniques are examined in this...
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To improve the accuracy and stability of multi-target tracking tasks for UAV swarm, a UAV scheduling method based on multi-agent reinforcement learning is proposed. In the proposed method, a multi-agent actor-critic w...
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作者：
Xu, YuanyeLiu, YuanhaoUniversity of Chinese Academy of Sciences
Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Acad. of Sci. Institutes for Robotics and Intelligent Mfg. Chinese Academy of Sciences Shenyang China
Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection o...
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Stochastic disturbances are inevitable for realworld robot systems, so for safe and efficient robot control, a stochastic robot motion model is required. However, it is not enough to simply estimate the stochastic rob...
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ISBN:
(纸本)9798350342291
Stochastic disturbances are inevitable for realworld robot systems, so for safe and efficient robot control, a stochastic robot motion model is required. However, it is not enough to simply estimate the stochastic robot motion model because underestimating the probability variation of control increases the risk of robot collisions. Conversely, overestimating the probability variation of control can decrease this risk, but the controller becomes conservative and less efficient. Therefore, in this paper, we propose a safe and accurate online estimation method for the diffusion term in the stochastic differential equation of a two-wheeled mobile robot. The proposed method utilizes model uncertainty to estimate a reasonably conservative model in the early stages of learning and then gradually improves the efficiency. Simulations and real-world experiments show that the proposed method can achieve higher estimation accuracy than other methods while keeping the underestimation of the robot's motion disturbance consistently at low level and gradually approaching the optimal solution as the training progresses.
Simulation tools are important auxiliary tools for scientific research. Design method of a practical and convenient simulation software for visual simulation of collaborative formation control for mobile robot cluster...
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Four-legged animals are able to change their gaits adaptively for lower energy consumption. However, designing a robust controller for their robot counterparts with multi-modal locomotion remains challenging. In this ...
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ISBN:
(纸本)9781728196817
Four-legged animals are able to change their gaits adaptively for lower energy consumption. However, designing a robust controller for their robot counterparts with multi-modal locomotion remains challenging. In this paper, we present a hierarchical control framework that decomposes this challenge into two kinds of problems: high-level decision-making for gait selection and robust low-level control in complex application environments. For gait transitions, we use reinforcement learning (RL) to design a gait policy that selects the optimal gaits in different environments. After the gait is decided, model predictive control (MPC) is applied to implement the desired gait. To improve the robustness of the locomotion, a model adaptation policy is developed to optimize the input parameters of our MPC controller adaptively. The control framework is first trained and tested in simulation, and then it is applied directly to a quadruped robot in real without any fine-tuning. We show that our control framework is more energy efficient by choosing different gaits and is more robust by adjusting model parameters compared to baseline controllers.
Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some i...
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