This is the IET Cyber-systems and robotics special issue of Autonomous systems:Navigation,learning,and *** systems,as very important representatives of Artificial Intelligence technologies,combine mechanical and elect...
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This is the IET Cyber-systems and robotics special issue of Autonomous systems:Navigation,learning,and *** systems,as very important representatives of Artificial Intelligence technologies,combine mechanical and electronic hardware,operating system,low-level dynamic control,and high-level intelligent decision components to address challenges that demand high-level autonomy and ma-chine intelligence.
This study focuses on a three-dimensional measurement system of components on a printed-circuit-board (PCB). Most existing methods for automatic optical inspection face the problem of light reflecting on components. W...
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People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centere...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MATErobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MATEViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MATErobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at MATErobot.
In the point cloud 3D object detection task, the point cloud data is unevenly distributed in 3D space. Therefore, whether it is regular sparse convolution or submanifold sparse convolution, there is a significant diff...
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ISBN:
(数字)9798350388077
ISBN:
(纸本)9798350388084
In the point cloud 3D object detection task, the point cloud data is unevenly distributed in 3D space. Therefore, whether it is regular sparse convolution or submanifold sparse convolution, there is a significant difference in the detection performance when dealing with objects at different positions using sparse convolutional networks. Therefore, this paper proposes an improvement using focal sparse convolution to break the limitations of traditional convolution methods and enhance the representation ability of sparse convolution. Focal sparse convolution is utilized to replace the currently better-performing two-stage object detection network based on the combination of points and voxels, enhancing the end-to-end training effectiveness and achieving better detection results. And an optional detachable auxiliary network is added, which, during the training stage, combines point features to guide the backbone network in learning the structural information of the 3D point cloud, enhancing the localization accuracy of the backbone network. Extensive experiments have been conducted on the KITTI dataset to validate the proposed method, yielding promising detection results and demonstrating the effectiveness of the approach.
This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite *** is assumed that the topology for the virtual leader and ...
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This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite *** is assumed that the topology for the virtual leader and followers is *** additional condition of the so-called barrier function is designed to make all agents move within a limited area.A novel continuous finite-time path-following control law is first designed based on the barrier function and *** a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as *** settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.
Software integration is central to successfully developing, deploying, and operating robotics applications. Yet, the particular integration process and its challenges are poorly understood. The continuous evolution of...
Software integration is central to successfully developing, deploying, and operating robotics applications. Yet, the particular integration process and its challenges are poorly understood. The continuous evolution of the robotics sector, incorporating constantly a growing number of technologies, makes the unification of processes increasingly complicated. Nevertheless, current research on robotics software integration largely focuses on specific integration activities instead of considering the overall activity as a process. To provide some insight into the state of robotics software integration, we drove a survey among researchers and practitioners in the field. In this survey, we inquired how robotics software integration is currently performed in order to identify similarities with traditional software development methodologies. Through this study, we discovered commonalities in the phases of the process and potential directions of a future research to address the current challenges in the area.
Visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years. However, most proposed visual localization systems assume that the surrounding enviro...
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Visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years. However, most proposed visual localization systems assume that the surrounding environment is static, which is difficult to maintain in real-world scenarios due to the presence of moving objects. In this paper, we present DFR-SLAM, a real-time and accurate RGB-D SLAM based on ORB-SLAM2 that achieves satisfactory performance in a variety of challenging dynamic scenarios. At the core of our system lies a motion consensus filtering algorithm estimating the initial camera pose and a graph-cut optimization framework combining long-term observations, prior information, and spatial coherence to jointly distinguish dynamic and static visual features. Other systems for dynamic environments detect dynamic components by using the information from short time-span frames, whereas our system uses observations from a long period of keyframes. We evaluate our system using dynamic sequences from the public TUM dataset, and the evaluation demonstrates that the proposed system outperforms the original ORB-SLAM2 system significantly. In addition, our system provides competitive localization accuracy with satisfactory real-time performance compared to closely related SLAM systems designed to adapt to dynamic environments.
Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sens...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.
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