Configuration Synthesis and Grasping Performance Analysis of Multi-Loop Coupling Capture Mechanism for Launch and Recovery of Torpedo-Shaped Autonomous Underwater Vehicle

Research of capture mechanisms with strong capture adaptability and stable grasp is important to solve the problem of launch and recovery of torpedo-shaped autonomous underwater vehicles(AUVs).A multi-loop coupling capture mechanism with strong adaptability and high retraction rate has been proposed for the launch and recovery of torpedo-shaped AUVs with different morphological features.Firstly,the principle of capturing motion retraction is described based on the appearance characteristics of torpedo-shaped AUVs,and the configuration synthesis of the capture mechanism is carried out using the method of constrained chain synthesis.Secondly,the screw theory is employed to analyze the degree of freedom(DoF)of the capture mechanism.Then,the 3D model of the capture mechanism is established,and the kinematics and dynamics simulations are carried out.Combined with the capture orientation requirements of the capture mechanism,the statics and vibration characteristics analyses are carried out.Furthermore,considering the capture process and the underwater working environment,the motion characteristics and hydraulics characteristics of the capture mechanism are analyzed.Finally,a principle prototype is developed and the torpedo-shaped AUVs capture experiment is completed.The work provides technical reserves for the research and development of AUV capture special equipment.

Global and International Security Under Spatial Grasp Paradigm

Global and international security cannot be provided from a single point or a set of separate points whatever powerful these might be(even with quantum supercomputers!).It should rather be deeply embedded and integrated with bodies of real systems wherever in physical,virtual,or combined spaces they may exist.So global security capabilities should not only be distributed,but rather be really spatial,self-organized,and dynamic,also exhibiting overall integrity,awareness,and consciousness features.The paper describes applicability of the patented and revealed in 10 books Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)which conceptually and functionally match security problems of large distributed and heterogeneous systems.It investigates very practical security solutions for finding and tracing distribution of forbidden items,world roaming criminals,recovery from natural and human-made disasters,tracing and elimination of moving dangerous objects in terrestrial and celestial spaces,as well as analysis and restoration of damaged transport networks.It advises how different security infrastructures can be organized and managed,and how to cooperate and integrate within global security systems with higher awareness and consciousness levels over them.The provided security-oriented version of SGL can be quickly implemented and integrated with existing distributed management and security systems.

基于GRASPS设计工具的八年级英语项目化教学实践研究——以“情景剧表演”为例

《英语课程标准》的基本原则中提到:要加强课程综合,注重关联,突出实践。然而传统的英语课堂只重视学生对英语知识的机械输入和积累,忽视学生英语学习过程的启发,特别是英语语言的实践活动,导致学生被动学习,课堂教学低效。笔者基于八年级学生的认知特点,以生活化的情景剧为表现形式,依托GRASPS设计工具和项目化学习进行教学改革,创设真实情境、突出学生主体、建立有效评价,给学生主动参与、体验、实践的机会,提升学生运用所学语言和跨学科知识创造性解决问题的能力。

基于GRASP-PR混合算法的多目标EV充电任务序列优化模型

提出基于GRASP-PR混合算法的多目标EV充电任务序列优化模型,以提高配电网的平峰填谷能力,确保其稳定运行。根据EV出行特点,在分析无序充电模式对配电网负荷峰谷差影响的基础上,构建EV充电任务序列优化模型,采用引入路径重连策略的贪心随机自适应搜索算法对优化模型求解,确定最优EV充电任务序列调度方案。实验结果表明:在无序充电模式下,并网充电EV数量越多,配电网负荷峰谷差越大;该优化模型可实现EV充电任务序列的优化控制,达到平峰填谷效果,降低配电网运行成本、EV充电成本。

A new breakthrough:ESD using a newly developed grasping type scissor forceps for early gastrointestinal tract neoplasms

Endoscopic submucosal dissection(ESD) has allowed the achievement of histologically curative en bloc resection of gastrointestinal neoplasms regardless of size,permitting the resection of previously non-resectable tumors.The ESD technique for treatment of early gastric cancer has spread rapidly in Japan and a few other Asian countries due to its excellent eradication rate compared to endoscopic mucosal resection.Although numerous electrosurgical knives have been developed for ESD,technical difficulties and high complication rates(bleeding and perforation) have limited their use worldwide.We developed the grasping type scissor forceps(GSF) to resolve such ESD-related problems.Our animal and preliminary clinical studies showed that ESD using GSF is a safe(no intraoperative complication) and technically efficient(curative en bloc resection rate 92%) method for dissection of early gastrointestinal tumors.The use of GSF is a promising option for performing ESD on early stage GI tract tumors both safely and effectively.

FORCE OPTIMIZATION OF GRASPING BY ROBOTIC HANDS

It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space of joint torques. A measure of grasping performanceis presented to protect joint actuators from working in heavy payloads. The joint torques arecalculated for the optimal performance under the frictional constraints and the physical limits ofmotor outputs. By formulating the grasping forces into the explicit function of joint torques, thefrictional constraints imposed on the grasping forces are transformed into the constraints on jointtorques. Without further simplification, the nonlinear frictional constraints can be simply handledin the process of optimization. Two numerical examples demonstrate the simplicity and effectivenessof the approach.

Stable grasping gesture analysis of a cable-driven underactuated robotic hand

The stable grasping gesture of a novel cable-driven robotic hand is analyzed. The robotic hand is underactuated, using tendon-pulley transmission and a parallel four-linkage mechanism to realize grasp. The structure design and a basic grasping strategy of one finger was introduced. According to the established round object enveloping grasp model, the relationship between the contacting and driving forces in a finger and stable grasping conditions were expounded. A method of interpolation and iteration was proposed to obtain the stable grasping gesture of the cable-driven hand grasping a round target. Quasi-statics analysis in ADAMS validated the variation of grasping forces, which illustrated the feasibility and validity of the proposed analytical method. Three basic types of grasping gestures of the underactuated hand were obtained on the basis of the relationship between the contact forces and position of a grasped object.

A New Grasping Mode Based on a Sucked-type Underactuated Hand

Robot hands have been developing during the last few decades. There are many mechanical structures and analyti?cal methods for di erent hands. But many tough problems still limit robot hands to apply in homelike environment. The ability of grasping objects covering a large range of sizes and various shapes is fundamental for a home service robot to serve people better. In this paper, a new grasping mode based on a novel sucked?type underactuated(STU) hand is proposed. By combining the flexibility of soft material and the e ect of suction cups, the STU hand can grasp objects with a wide range of sizes, shapes and materials. Moreover, the new grasping mode is suitable for some situations where the force closure is failure. In this paper, we deduce the e ective range of sizes of objects which our hand using the new grasping mode can grasp. Thanks to the new grasping mode, the ratio of grasping size between the biggest object and the smallest is beyond 40, which makes it possible for our robot hand to grasp diverse objects in our daily life. For example, the STU hand can grasp a soccer(220 mm diameter, 420 g) and a fountain pen(9 mm diameter, 9 g). What’s more, we use the rigid body equilibrium conditions to analysis the force condition. Experiment evaluates the high load capacity, stability of the new grasping mode and displays the versatility of the STU hand. The STU hand has a wide range of applications especially in unstructured environment.

Dynamics and Stability of Blind Grasping of a 3-Dimensional Object under Non-holonomic Constraints

A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.

Hand-eye-vision based control for an inspection robot's autonomous line grasping

In order to ensure that the off-line arm of a two-arm-wheel combined inspection robot can reliably grasp the line in case of autonomous obstacle crossing,a control method is proposed for line grasping based on hand-eye visual servo.On the basis of the transmission line's geometrical characteristics and the camera's imaging principle,a line recognition and extraction method based on structure constraint is designed.The line's intercept and inclination are defined in an imaging space to represent the robot's change of pose and a law governing the pose decoupling servo control is developed.Under the integrated consideration of the influence of light intensity and background change,noise(from the camera itself and electromagnetic field)as well as the robot's kinetic inertia on the robot's imaging quality in the course of motion and the grasping control precision,a servo controller for grasping the line of the robot's off-line arm is designed with the method of fuzzy control.An experiment is conducted on a 1:1 simulation line using an inspection robot and the robot is put into on-line operation on a real overhead transmission line,where the robot can grasp the line within 18 s in the case of autonomous obstacle-crossing.The robot's autonomous line-grasping function is realized without manual intervention and the robot can grasp the line in a precise,reliable and efficient manner,thus the need of actual operation can be satisfied.

A novel grasping force control strategy for multi-fingered prosthetic hand

A grasping force control strategy is proposed in order to complete various free manipulations by using anthropomorphic prosthetic hand. The position-based impedance control and force-tracking impedance control are used in free and constraint spaces, respectively. The fuzzy observer is adopted in transition in order to switch control mode. Two control modes use one position-based impedance controller. In order to achieve grasping force track, reference force is added to the impedance controller in the constraint space. Trajectory tracking in free space and torque tracking in constrained space are realized, and reliability of mode switch and stability of system are achieved. An adaptive sliding mode friction compensation method is proposed. This method makes use of terminal sliding mode idea to design sliding mode function, which makes the tracking error converge to zero in finite time and avoids the problem of conventional sliding surface that tracking error cannot converge to zero. Based on the characteristic of the exponential form friction, the sliding mode control law including the estimation of friction parameter is obtained through terminal sliding mode idea, and the online parameter update laws are obtained based on Lyapunov stability theorem. The experiments on the HIT Prosthetic Hand IV are carried out to evaluate the grasping force control strategy, and the experiment results verify the effectiveness of this control strategy.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Endoscopic submucosal dissection of a rectal carcinoid tumor using grasping type scissors forceps

Endoscopic submucosal dissection （ESD） with a knife is a technically demanding procedure a ssociated with ahigh complication rate. The shortcomings of this meth-od are the inability to fix the knife to the target lesion,and compression of the lesion. These can lead to major complications such as perforation and bleeding. To reduce the risk of complications related to ESD, we developed a new grasping type scissors forceps （GSF）,which can grasp and incise the targeted tissue using electrosurgical current. Colonoscopy on a 55-year-old woman revealed a 10-ram rectal submucosal nodule.The histological diagnosis of the specimen obtained by biopsy was carcinoid tumor. Endoscopic ultrasonog-raphy demonstrated a hypoechoic solid tumor limitedto the submucosa without lymph node involvement. Itwas safely and accurately resected without unexpectedincision by ESD using a GSF. No delayed hemorrhage or perforation occurred. Histological examination confirmed the carcinoid tumor was completely excisedwith negative resection margin.

Configuration Design of an Under-Actuated Robotic Hand Based on Maximum Grasping Space

Capture is a key component for on?orbit service and space debris clean. The current research of capture on?orbit focuses on using special capture devices or full?actuated space arms to capture cooperative targets. However, the structures of current capture devices are complex, and both space debris and abandoned spacecraft are non?cooperative targets. To capture non?cooperative targets in space, a lightweight, less driven under?actuated robotic hand is proposed in this paper, which composed by tendon?pulley transmission and double?stage mechanisms, and always driven by only one motor in process of closing finger. Because of the expandability, general grasping model is constructed. The equivalent joint driving forces and general grasping force are analyzed based on the model and the principle of virtual work. Which reveal the relationship among tendon driving force, joint driving forces and grasping force. In order to configure the number of knuckles of finger, a new analysis method which takes the maximum grasping space into account, is proposed. Supposing the maximum grasped object is an envelope circle with diameter of 2.5m. In the condition, a finger grasping maximum envelope circle with different knuckles is modeled. And the finger lengths with corresponding knuckles are calculated out. The finger length which consists of three knuckles is the shortest among under?actuated fingers consists of not more than five knuckles. Finally, the principle prototype and prototype robotic hand which consists of two dingers are designed and assembled. Experiments indicate that the under?actuated robotic hand can satisfy the grasp requirements.

Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning

Directly grasping the tightly stacked objects may cause collisions and result in failures,degenerating the functionality of robotic arms.Inspired by the observation that first pushing objects to a state of mutual separation and then grasping them individually can effectively increase the success rate,we devise a novel deep Q-learning framework to achieve collaborative pushing and grasping.Specifically,an efficient non-maximum suppression policy(PolicyNMS)is proposed to dynamically evaluate pushing and grasping actions by enforcing a suppression constraint on unreasonable actions.Moreover,a novel data-driven pushing reward network called PR-Net is designed to effectively assess the degree of separation or aggregation between objects.To benchmark the proposed method,we establish a dataset containing common household items dataset(CHID)in both simulation and real scenarios.Although trained using simulation data only,experiment results validate that our method generalizes well to real scenarios and achieves a 97%grasp success rate at a fast speed for object separation in the real-world environment.

Robot Automatic Grasping Based on Finger-Tip Laser Range Finders

Finger-tip range finders can be used to modify robot end effector's position & attitude and avoid collision. This paper introduces the development of finger tip short dis tance laser range finders for space robot multisensory gripper, including the sensors' measuring principle, signal processing circuit, etc. By using these sensors' informations the gripper can be automatically ajusted to grasp the object steadily and reliablly even if it is badly positioned.

Imaginary displacement method for analysis of multi-fingered robot hands grasping stability

Gives an overview of the present status of researches on grasp stability of multi fingered dexterous robot hands,presents the imaginary displacement method for evaluating the grasp stability, which is easy to realize on computer,and has no limit on contact points for each finger. Analyses for grasping stability with single contact point of typical objects with different curvature proved the effectiveness of the method proposed and optimal grasp examples are given as well.

A Comparison of Pinch Force between Finger and Palm Grasp techniques in Laparoscopic Grasping

Laparoscopic surgery is a new abdominal surgical procedure which helps the patients in many ways like less hospital stay, faster recovery and reduced pain. The main disadvantage in this surgical procedure is the reduced haptic perception by the surgeons due to the usage of laparoscopic instrument to handle tissues which in turn cause damage of it as compared to an open surgery. The primary aim of this investigation was to compare the pinch force applied during two different methods of laparoscopic grasping: Finger and Palm grasp. A low cost force sensing resistor tailor made for the grasper tip was designed and fabricated for quantifying the grasper tip force in the study. The results indicate more pinch force was applied during palm grasp as compared to finger grasp so as to prevent the slippage of the tissues from the jaws of the laparoscopic graspers.

Effects of Band Therapy Using Music on Grasping Power, Depression, and Personal Relationships in Nursing-Home-Dwelling Elderly Individuals

The current study examined the effects of band therapy using music on grasping power, depression, and personal relationships among residents of a nursing home. Thirty subjects participated in the study. The band therapy included greetings, warm-up exercises with music, singing with dance, playing instruments, closing speech, and stretching with background music. Band therapy was held for 40 minutes once per week, for a total of four sessions, in the activity room of the nursing home. Findings showed that grasping power, depression, and personal relationships were improved at posttest, but the differences were not statistically significant. A better study design to compare the effects of band therapy with the other group, and a more simple and repeated intervention for the elderly to follow without stress might be necessary.

Object Grasping Detection Based on Residual Convolutional Neural Network

Robotic grasps play an important role in the service and industrial fields,and the robotic arm can grasp the object properly depends on the accuracy of the grasping detection result.In order to predict grasping detection positions for known or unknown objects by a modular robotic system,a convolutional neural network(CNN)with the residual block is proposed,which can be used to generate accurate grasping detection for input images of the scene.The proposed model architecture was trained on the standard Cornell grasp dataset and evaluated on the test dataset.Moreover,it was evaluated on different types of household objects and cluttered multi-objects.On the Cornell grasp dataset,the accuracy of the model on image-wise splitting detection and object-wise splitting detection achieved 95.5%and 93.6%,respectively.Further,the real detection time per image was 109 ms.The experimental results show that the model can quickly detect the grasping positions of a single object or multiple objects in image pixels in real time,and it keeps good stability and robustness.