The ability to handle contact makes robots qualified for many complicated tasks, such as welding, hammering, and wiping. Robot cameras facilitate position planning and control without the geometric knowledge of contac...
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ISBN:
(纸本)9798350377712;9798350377705
The ability to handle contact makes robots qualified for many complicated tasks, such as welding, hammering, and wiping. Robot cameras facilitate position planning and control without the geometric knowledge of contact surfaces since they can project contact surfaces onto a 2-dimensional image plane. However, existing hybrid vision-force control (HVFC) methods still rely on this knowledge to project the force on the constraint subspace and do not adequately leverage the redundant degrees of freedom (DoFs) for redundant robots with contact tasks. This paper proposes an enhanced HVFC solution for redundant robots equipped with an eye-to-hand camera to unify HVFC in the Cartesian space and impedance control in the joint nullspace into one closed-loop dynamics with rigorous stability guarantees. Any geometric knowledge of contact surfaces is not required by projecting the force into the redundant space of the visual task rather than the surface's normal space. Experiments on a seven-DoF collaborative robot have verified that the proposed method is qualified for simultaneous contact tasks in the Cartesian space and compliant interaction in the joint nullspace.
This paper is mainly the smart home system and some other intelligent devices and robot technology organic integration together, so that it can be more comprehensive for the robot service. At the same time, computer c...
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In this study, we propose a shared control method for teleoperated mobile robots using brain-machine interfaces (BMI). The control commands generated through BMI for robot operation face issues of low input frequency,...
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ISBN:
(纸本)9798350377712;9798350377705
In this study, we propose a shared control method for teleoperated mobile robots using brain-machine interfaces (BMI). The control commands generated through BMI for robot operation face issues of low input frequency, discreteness, and uncertainty due to noise. To address these challenges, our method estimates the user's intended goal from their commands and uses this goal to generate auxiliary commands through the autonomous system that are both at a higher input frequency and more continuous. Furthermore, by defining the confidence level of the estimation, we adaptively calculated the weights for combining user and autonomous commands, thus achieving shared control. We conducted navigation experiments in both simulated environments and participant experiments in real environments including user ratings, using a pseudo-BMI setup. As a result, the proposed method significantly reduced obstacle collisions in all experiments. It markedly shortened path lengths under almost all conditions in simulations and, in participant experiments, especially when user inputs become more discrete and noisy (p<0.01). Furthermore, under such challenging conditions, it was demonstrated that users could operate more easily, with greater confidence, and at a comfortable pace through this system.
Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact for...
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ISBN:
(纸本)9798350377712;9798350377705
Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at https://***/omron-sinicx/CompACT.
This paper focuses on enhancing the security of a chosen system by implementing multiple layers of protection, including concealing sensitive data on the Liquid Crystal Display (LCD), utilizing hashed Unique Identifie...
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This study aims to address the issue of end-to-end trusted connections in critical control operations of power systems, particularly in complex environments spanning multiple trust domains. With the accelerated digita...
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We present a shared control framework for teleoperation that combines the human and autonomous robot agents operating in different dimension spaces. The shared control problem is an optimization problem to maximize th...
ISBN:
(纸本)9798350336702
We present a shared control framework for teleoperation that combines the human and autonomous robot agents operating in different dimension spaces. The shared control problem is an optimization problem to maximize the human's internal action-value function while guaranteeing that the shared control policy is close to the autonomous robot policy. This results in a state update rule that augments the human controls using the Riemannian metric that emerges from computing the curvature of the robot's value function to account for any cost terms or constraints that the human operator may neglect when operating a redundant manipulator. In our experiments, we apply Linear Quadratic Regulators to locally approximate the robot policy using a single optimized robot trajectory, thereby preventing the need for an optimization step at each time step to determine the optimal policy. We show preliminary results of reach-and-grasp teleoperation tasks with a simulated human policy and a pilot user study using the VR headset and controllers. However, the mixed user preference ratings and quantitative results show that more investigation is required to prove the efficacy of the proposed paradigm.
This overview aims to provide a comprehensive insight into the manufacturing, design, and selection of LED drivers, highlighting the significance of LEDs in lighting technology. Compared to traditional light sources l...
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Roads are transport infrastructure, and regular maintenance ensures safety and efficiency. Deteriorating asphalt can lead to potholes, increasing the risk of accidents. Therefore, this paper evaluates the deep learnin...
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The project '10GB MAC CORE Verification - Reference (input monitor) Module' involves the verification of a 10 gigabit Ethernet Media Access control (MAC) core. Specifically, this study focuses on the reference...
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