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Carbon Black/PDMS Based Flexible Capacitive Tactile Sensor for Multi-Directional Force Sensing
No full textFlexible sensing tends to be widely exploited in the process of human-computer interactions of intelligent robots for its contact compliance and environmental adaptability. A novel flexible capacitive tactile sensor was proposed for multi-directional force sensing, which is based on carbon black/polydimethylsiloxane (PDMS) composite dielectric layer and upper and lower electrodes of carbon nanotubes/polydimethylsiloxane (CNTs/PDMS) composite layer. By changing the ratio of carbon black, the resolution of carbon black/PDMS composite layer increases at 4 wt%, and then decreases, which was explained according to the percolation theory of the conductive particles in the polymer matrix. Mathematical model of force and capacitance variance was established, which can be used to predict the value of the applied force. Then, the prototype with carbon black/PDMS composite dielectric layer was fabricated and characterized. SEM observation was conducted and a ratio was introduced in the composites material design. It was concluded that the resolution of carbon sensor can reach 0.1 N within 50 N in normal direction and 0.2 N in 0-10 N in tangential direction with good stability. Finally, the multi-directional force results were obtained. Compared with the individual directional force results, the output capacitance value of multi-directional force was lower, which indicated the amplitude decrease in capacity change in the normal and tangential direction. This might be caused by the deformation distribution in the normal and tangential direction under multi-directional force
Binocular Feature Fusion and Spatial Attention Mechanism Based Gaze Tracking
No full textGaze tracking is widely used in driver safety driving, visual impairment detection, virtual reality, human robot interaction, and reading process tracking. However, varying illumination, various head poses, different distances between human and cameras, occlusion of hair or glasses, and low-quality images pose huge challenges to accurate gaze tracking. In this article, based on binocular feature fusion and convolution neural network, a novel method of gaze tracking is proposed, in which local binocular spatial attention mechanism (LBSAM) and global binocular spatial attention mechanism (GBSAM) are integrated into the network model to improve the accuracy. Furthermore, the proposed method is validated on the GazeCapture database. In addition, four groups of comparative experiments have been conducted: between binocular feature fusion model and binocular data fusion model; among the local binocular spatial attention model, the local binocular channel attention model, and the model without local binocular attention mechanism; between the model with GBSAM and that without GBSAM; and between the proposed method and other state-of-the-art approaches. The experimental results verify the advantages of binocular feature fusion, LBSAM and GBSAM, and the effectiveness of the proposed method
基于增强现实的复杂易变形舱段对接可视化装配辅助方法
No full text本发明公开了一种基于增强现实的复杂易变形舱段对接可视化装配辅助方法，涉及智能装配领域。该方法利用实测的舱段三维模型构建虚拟装配仿真环境，进行配合间隙及干涉状态检测；采用装配路径智能优化算法，通过标准通讯接口协议与虚拟装配仿真环境进行信息交互，完成最优装配路径规划；将装配引导路径和视觉检测得到的实际装配路径以3D图像、路径偏差数值等形式叠加到虚实配准后的增强现实环境中，实现可视化智能装配辅助功能。本发明可解决复杂易变形舱段对接过程中频繁出现的卡死错位等装配难题，降低对操作人员经验技能的依赖，大幅提高装配效率及一次装配成功率
一种水下腿履复合爬行底盘及应用其的水下机器人
No full text本发明公开了一种水下腿履复合爬行底盘及应用其的水下机器人，涉及水下移动设备技术领域，解决了现有的水下机器人遇到障碍物时一般需要转向后绕开，由于其无法快速地跨过该障碍物，因此其缺乏在水中快速躲避障碍并保持快速行驶能力的问题，其技术方案要点是，包括：主架体、行驶机构、具有折叠状态和展开状态的折叠机构，其中折叠机构包括：第一转动装置、第一连杆、第二转动装置、第二连杆；第一连杆与第一转动装置传动连接；第二连杆与第二转动装置传动连接，第一连杆与第二连杆转动连接，本发明提供了一种新的水下快速移动的具体实施方式，其带可折叠式收纳功能、具有良好躲避障碍物能力、良好通过性、自由度高、结构简单、便于生产制造的优点
基于计算机视觉的细胞计数方法
No full text本发明涉及基于计算机视觉的细胞计数方法，包括以下步骤：S1、采集经过染色的切片样本的RGB原图E0，并分别处理成灰度图E1、蓝色通道图E18；S2、对原图E0进行图像处理获取染色细胞图E6，对染色细胞进行计数；S3、对灰度图E1进行图像处理获取包含染色和非染色的全部细胞图E16，对全部细胞进行计数；S4、对蓝色通道图E18进行图像处理获取图像E21，按照给定的不同划界区域对染色细胞进行计数，计算像素面积数，精确标画该区域外围轮廓；S5、可视化展示以上各图像和计数，用于直观展示和细胞计数
多水下机器人最大探测覆盖率的快速梳型路径规划方法
No full text本发明涉及到多水下机器人路径规划技术领域，尤其设计一种多自主水下机器人基于探测区域覆盖率的路径规划方法。包括以下步骤：基于待探测区域的地形条件以及自主水下机器人的探测能力，构建二维栅格模型；基于二维栅格模型，采用分步迭代的方式确定单体自主水下机器人的探测轨迹，进而得到单体自主水下机器人进行梳型探测时的探测面积；利用单体自主水下机器人进行梳型探测时的探测面积，结合粒子群优化算法，得到满足多自主水下机器人最大探测覆盖率时的路径规划。本方法时刻计算自主水下机器人当前航线与所有登高线关系并确定安全的方法。使得规划更快速，适合海上不断变化的探测环境，具有很高的工程意义
一种潜航器最优容错自适应控制分配方法
No full text本发明涉及一种潜航器最优容错自适应控制分配方法，该方法将控制器输出的综合力和力矩以阻力最优的方式分配到各个执行机构，该方法包括两个约束优化求解阶段：基于约束松弛的可行解判定阶段和以阻力最小为目标的最优分配求解阶段。第一阶段，基于约束松弛的可行解判断阶段，将综合力和力矩约束条件进行松弛，将执行机构指令值和松弛变量共同作为决策变量，以松弛变量平方和最小为目标进行求解，获取松弛后的约束条件，从而保证第二阶段存在可行解；第二阶段，以阻力最小为目标的最优分配阶段，将松弛后的约束条件作为约束条件，求解各执行机构的最优指令值，从而实现潜航器减阻节能航行，且对一定程度的执行机构故障或性能退化情况具有自适应能力
Incremental Learning Framework for Autonomous Robots Based on Q-Learning and the Adaptive Kernel Linear Model
No full textThe performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q-learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursive least squares (L2-KRLS) algorithm. The AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local epsilon-greedy policy is proposed to speed the convergence rate of Q-learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve-fitting example shows that the L2-KRLS-based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local epsilon-greedy policy-based Q-learning is faster than the existing Q-learning algorithms
Effect of Stress Wave Propagation on Surface Stress Relaxation in 7050 Aluminum Panel Shocked by a Nanosecond Laser
No full textIntegral panel is an important structure to reduce aircraft weight. Nanosecond laser beams can be used for the forming and surface modification in panels. To explore the propagation of laser-induced stress waves and their influence on surface stress relaxation, a simulation model is used to capture the propagation process of stress waves. The results show that the stress wave first decays rapidly, and then the attenuation rate decreases gradually. In the initial propagation stage, the attenuation of the stress wave can be fitted by sigma(x)=Ee(-kx); then, after propagating a certain distance, the stress wave amplitude is more suitable to sigma(x)= Ex(-k). During the propagation processing, the stress wave is reflected on the shocked and back surface. A tensile stress wave is formed by the reflection of the incident compressive stress wave, and the value of the stress wave is improved. The stress wave has a great influence on the residual stress distribution in 1mm thin panels. When the stress wave is reflected on the back surface, a stress is induced into the surface. When the stress wave is reflected by the shocked surface, the stress on the surface is relaxed because the incident wave is a tensile wave. Therefore, when a nanosecond laser is used in thin panels, the shock wave has an important influence on the stress distribution, which needs to be considered in engineering applications
SiamOAN: Siamese object-aware network for real-time target tracking
No full textExisting Siamese-based tracking algorithms usually utilize local features to represent the object, which lack sufficient discrimination and may degrade tracking performance in challenging situations. To address this issue, we propose a novel object-aware network to improve feature representation and achieve robust object tracking. The proposed object-aware network contains a background filter module (BFM), channel complementary module (CCM), and template adaptive network (TAN). Specifically, by locating the target in the initial frame on the feature maps, BFM suppresses the background interference of the target template. CCM captures the global context by exploring the complementary information of each channel. The lightweight TAN adaptively recognizes valuable features for the target and represents the target template just through a single vector. Benefiting from these three components, the object-aware network enhances the discrimination of feature maps and alleviates background interference to some extent. The proposed object-aware network could be integrated with the Siamese-based backbone network for real-time object tracking, named SiamOAN. Extensive experiments on the six challenging benchmarks including OTB100, UAV123, VOT2016, VOT2018, GOT10k, and LaSOT, show that the pro-posed SiamOAN outperforms many state-of-the-art trackers and runs at approximately 67 fps on GPU RTX3090. CO 2021 Published by Elsevier B.V