In the electronic industry product quality control, PCB defect detection is a crucial part, which has the characteristics of small defect size and high similarity. The existing defect detection methods are still not g...
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作者：
Xinrui WuJianbo XuPuyuan HuGuangming WangHesheng WangDepartment of Automation
Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Department of Engineering
University of Cambridge Cambridge U.K
Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. diffic...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://***/IRMVLab/LHMap-loc.
Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability ma...
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作者：
Jiwei ShanYirui LiQiyu FengDitao LiLijun HanHesheng WangDepartment of Automation
Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering
Shanghai Jiao Tong University Shanghai China
Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existin...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field a...
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作者：
Wenhua WuGuangming WangJiquan ZhongHesheng WangZhe LiuMoE Key Lab of Artificial Intelligence
AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation
Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Insititute of Medical Robotics Shanghai Jiao Tong University Shanghai China
Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are indepen...
Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://***/IRMVLabIPLPE-Depth.
Soft robots are usually driven by soft actuators, and the dielectric elastomer actuator (DEA) is recognized as one of the most promising soft actuators. However, the DEA has complex nonlinear char- acteristics, which ...
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Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining c...
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ISBN:
(数字)9798331509644
ISBN:
(纸本)9798331509651
Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant; on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A* which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion.
sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for th...
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作者：
Wang, GuangmingTian, XiaoyuDing, RuiqiWang, HeshengDepartment of Automation
Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China
Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of sce...
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