Modeling the dynamics of continuum robotic arms is challenging due to the highly nonlinear nature and uncertain and complicated inherent structure. This affects the application of continuum arms in various aspects, su...
Modeling the dynamics of continuum robotic arms is challenging due to the highly nonlinear nature and uncertain and complicated inherent structure. This affects the application of continuum arms in various aspects, such as inverse kinematics, path generation, optimization, and control. This paper deals with the dynamics modeling of continuum robotic arms using screw theory to develop a suitable model with high accuracy, speed, and low computational load to achieve real-time control. By using the representation of the Lie group of screws in the screw theory, which simplifies the mathematics of the problem, it is possible to achieve a real-time integrated representation of the dynamics of the deformation of the body of continuum robotic arms. This provides the possibility of accurately predicting the system behavior even in the presence of external forces acting on the end-effector. The experimentally validated results for circular and infinite motion trajectories show the model effectiveness in enhancing the trajectory tracking performance and ability to estimate configuration shape changes under external loads. The integration of this model in soft robotic arms has the potential to revolutionize their application in a wide range of industries and medical applications.
Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having ...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a high-performance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.
robotics and artificial intelligence (AI) are rapidly transforming many industries and aspects of our lives, and sport activities are no exception. Robots and AI are already being used in a variety of sports, includin...
robotics and artificial intelligence (AI) are rapidly transforming many industries and aspects of our lives, and sport activities are no exception. Robots and AI are already being used in a variety of sports, including training and performance analysis. This paper introduces a new assistant ping-pong robot (RoboPing) that shoots ping-pong balls with different speeds and spins, besides using AI to analyze training sessions and providing useful insights into the game. RoboPing represents a significant advancement in mechanical design over previous versions, offering enhanced functionality. Being equipped with a precise vision system makes RoboPing more advanced compared to existing versions.
The increment in elder population has been the main cause of exoskeletons and rehabilitation robots development. Studies have proven that assistive robots are more effective in limb empowerment and rehabilitation than...
The increment in elder population has been the main cause of exoskeletons and rehabilitation robots development. Studies have proven that assistive robots are more effective in limb empowerment and rehabilitation than traditional ways. A lower-limb assistive robot called RoboWalk has been designed in ARAS lab at K. N. Toosi University of technology to help compensate user weight when moving. This study reviews the RoboWalk weight assisting robot and illustrates its Test-Stand more thoroughly with updated modifications and parts. Test-Stand system features and operational specifications are then described. Then, system kinetics and kinematics are also explained. Finally, the weight assisting performance of RoboWalk is analyzed. Results show that the gait generating mechanism creates a singularity in Test-Stand system that causes a decrease in the assisting force. It is concluded that this issue is only related to the Test-Stand and does not appear in the actual RoboWalk robot. Nevertheless, Solutions to avoid this in Test-Stand including back and forth simulation of the gait and maximum motor torque limitation are also suggested.
Designed to provide rehabilitation or the assistance of walking for individuals who are with lower limb muscle injuries or spinal during their activities, self-balancing lower limb exoskeletons have been developed. Th...
Designed to provide rehabilitation or the assistance of walking for individuals who are with lower limb muscle injuries or spinal during their activities, self-balancing lower limb exoskeletons have been developed. This paper introduces an innovative controller for a fully actuated exoskeleton that ensures stable walking for wearers without the need for external devices. The exoskeleton robot is represented as a center of mass model, and based on this representation, the trajectory generation and controller are discussed, aiming to improve locomotion stability. To assess the stability and dynamic performance, a stable walking experiment with the exoskeleton was conducted, involving a male subject.
This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image cl...
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This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation.
In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point c...
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The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual Learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.
Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychol...
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ISBN:
(数字)9798350355123
ISBN:
(纸本)9798350355130
Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.
Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biol...
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