This paper presents a six-layer framework for smart retrofitting of legacy industrial machines to align with Industry 4.0 standards, using open-source technologies for cost-effectiveness and scalability. The layers in...
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ISBN:
(数字)9798350350821
ISBN:
(纸本)9798350350838
This paper presents a six-layer framework for smart retrofitting of legacy industrial machines to align with Industry 4.0 standards, using open-source technologies for cost-effectiveness and scalability. The layers include edge data acquisition, data preprocessing, Digital Twin technology, long-term data storage, AI-based data analysis, and real-time visual-ization. Implemented and evaluated through a case study on a legacy CNC lathe, the framework demonstrated reliable communication and effective performance across various layers. The results validate the framework's feasibility and highlight the need for further fine-tuning of open large language models with manufacturing domain knowledge.
Sampling-based planning algorithms such as RRT have been proved to be efficient in solving path planning problems for robotic systems. Various improvements to the RRT algorithm have been presented to improve the perfo...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Sampling-based planning algorithms such as RRT have been proved to be efficient in solving path planning problems for robotic systems. Various improvements to the RRT algorithm have been presented to improve the performance of the extension and convergence of the random trees, such as Informed RRT*. However, with the growth of spatial dimensions, the time consumption of randomly sampling the entire state space and incrementally rewiring the random trees raises drastically before a feasible solution is found. In this paper, to enhance the convergence performance of optimal solutions, we present Reconstructed Bi-directional Informed RRT* (RBI-RRT*) path planning algorithm. The algorithm acts as RRT-Connect to rapidly find a feasible solution, which helps compress the sampling space as Informed RRT* does. After the random trees are transformed into RRT* structure by the reconstruction process in RBI-RRT*, the algorithm continues to find the near-optimal path. A series of simulations and real-world robot experiments were conducted to evaluate the algorithm against existing planning algorithms. Compared to Informed RRT* Connect, RBI-RRT* reduced the computation time of achieving a specific cost by 22.1% on average in simulations and 11.2% in the real-world robotic arm experiments. The results show that RBI-RRT* is more efficient in high-dimensional planning problems.
The aim of this paper is to infer the development of a control and sensor strategy for an industrial wearable wrist exoskeleton through preliminary classification and prediction of workers' actions gathered in a r...
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ISBN:
(数字)9798350386523
ISBN:
(纸本)9798350386530
The aim of this paper is to infer the development of a control and sensor strategy for an industrial wearable wrist exoskeleton through preliminary classification and prediction of workers' actions gathered in a real working environment. The effort intensity, correlated with the exerted force, and sensor strategy optimization are considered and evaluated for design purposes. In this paper, an EMG-based wrist motion classification and a force prediction models are presented based on measurements taken on six healthy subjects in a manufacturing plant. A wrist motion pattern recognition algorithm has been developed on top of surface EMG data measured through an 8-channel commercially available sensor (Myo Armband). Moreover, a force regression model has been built based upon wrist force measurements detected by using a commercial hand-held dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy lays the basis for a more complex architecture which will be designed to control an active wrist exoskeleton for industrial applications, simplifying its design, costs and data process by adopting as few sensors and electronic components as possible while maintaining sufficient reliability and accuracy to provide adequate assistance.
We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous ma-nipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer...
We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous ma-nipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nearly 400 hand designs, testing grasp quality under external force disturbances. We validate the optimized designs in the real world through teleoperation of pickup and reorient manipulation tasks. Our real world evaluation, from over 900 teleoperated tasks, shows that the trend in design performance in simulation resembles that of the real world. Furthermore, we show that optimized hand designs from our approach outperform existing soft robot hands from prior work in the real world. The results highlight the usefulness of simulation in guiding parameter choices for anthropomorphic soft robotic hand systems, and the effectiveness of our automated design iteration approach, despite the sim-to-real gap.
Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuati...
Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuation methods of ESR still suffer from challenges including large dimensions, expensive, complex modeling and control, and limited payload capacity. In this paper, we introduce a novel design for a lightweight, extensible and variable stiffness soft robot-based cable-driven actuation without backbone. The design composes of one section with three segments based on semi-octagon honeycomb structure, wherein the middle segment acts like a soft spring to provide extension feature. Additionally, the top and bottom segments have identical structure with two honeycomb patterns embedded within each other to improve the stiffness of the design. A design analysis is conducted to optimize the proposed structure with respect to stress and displacement. Additionally, a shape estimation approach is utilized to get accurate inference of the prototype’s shape based on data acquired from a low-cost Inertial Measurement Unit (IMU) and Motion Capture System (MCS), and constant curvature assumption. A series of experiments including workspace reach-ability, repeatability, shape approximation and payload analysis are carried out to validate the proposed design. The results show that the prototype exhibits good compression/extension ratio up to 42% and 50% relative to its normal length, respectively. Moreover, its payload capability reaches to 565 grams.
Parallel manipulators found wide applications in the industry such as entertainment, heavy, and aerospace industries. However, the contemporary growth of the economy and industry demands additional and exceptional cap...
Parallel manipulators found wide applications in the industry such as entertainment, heavy, and aerospace industries. However, the contemporary growth of the economy and industry demands additional and exceptional capabilities for robots. Parallel manipulator applications in the industry are pretty specific and complex rather than serial manipulators. Therefore in this research, we proposed an isotropic Stewart parallel manipulator to enhance robot stiffness and payload capacity capabilities. Moreover, proves the concept by experimental methods. The main contribution of this paper is to rely on robot design, joint positions, and comparison of experimental results to prove the concept idea. The research paper is organized in the following order: introduction, design concept, joint positions and simulation, experimental results, and conclusion.
With the increasing popularity of electric vehicles (EV s), the research field of planning efficient routes for these vehicles is gaining growing attention. As there are a limited number of charging stations for EVs c...
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ISBN:
(数字)9798350361070
ISBN:
(纸本)9798350361087
With the increasing popularity of electric vehicles (EV s), the research field of planning efficient routes for these vehicles is gaining growing attention. As there are a limited number of charging stations for EVs compared to gas stations for fossil fuel vehicles, EV routing requires careful consideration of energy constraints and replenishment. The classical traveling salesperson problem (TSP) and vehicle routing problem (VRP) are known to be NP-hard, which means that the electric vehicle routing problem (EVRP), a similar problem with added energy constraints, is computationally even more challenging. Recently, reinforcement learning (RL) is being suggested as an effective tool that can alleviate the computational burden of challenging problems. This paper presents a RL-based method for solving routing problems with energy constraints. Multi-head attention mechanisms are employed for both the encoder and decoder, and a masking scheme is applied at the decoding phase in order to compute a feasible solution and minimize the energy constraint violation. This method generates an efficient route in which all task nodes are visited while meeting the energy requirements by visiting the charging stations when needed. The performance of the methodology is demonstrated through a Monte Carlo simulation, and the results are discussed and analyzed.
HIF in a power system can be due to a broken or unbroken distribution line in a power system. The fault possesses dynamic features, including nonlinearity, randomness, asymmetry, shoulder, buildup, and intermittence. ...
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The advancement in computing power has significantly reduced the training times for deep learning, fostering the rapid development of networks designed for object recognition. However, the exploration of object utilit...
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This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive proces...
This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive processes, lack of real-time monitoring, and excessive pesticide usage, In this paper, the YOLOv8 algorithm, representing the forefront of object detection methods is chosen for UAV wheat rust detection. We train the model on a prepared dataset. Subsequently, we evaluated 5 models, including YOLOv5, and YOLOv8, at two different scales (n, s) based on average precision (mAP@.5), precision, recall, and FPS. The experimental outcomes establish the superiority of the enhanced FasterNet-YOLOv8 model over YOLOv8 and YOLOv5 across all metrics, encompassing detection accuracy and processing speed. These findings affirm the viability of our suggested model within the UAV wheat rust monitoring system and underscore the efficacy of the model enhancements.
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