Fast grasping of unknown objects through automatic determination of the required number of mobile robots

In this paper, we present a strategy for fast grasping of unknown objects by mobile robots through automatic determination of the number of robots. An object handling system consisting of a Gripper robot and a Lifter robot is designed. The Gripper robot moves around an unknown object to acquire partial shape information for determination of grasping points. The object is transported if it can be lifted by the Gripper robot. Otherwise, if all grasping trials fail, a Lifter robot is used. In order to maximize use of the Gripper robot’s payload, the detected grasping points that apply the largest force to the gripper are selected for the Gripper robot when the object is grasped by two mobile robots. The object is measured using odometry and scanned data acquired while the Gripper robot moves around the object. Then, the contact point for calculating the insert position for the Lifter robot can be acquired quickly. Finally, a strategy for fast grasping of known objects by considering the transition between stable states is used to realize grasping of unknown objects. The proposed approach is tested in experiments, which find that a wide variety of objects can be grasped quickly with one or two mobile robots.     

抓取任务中的融差控制方法

依据“融差性思维”,提出了无需精确感知依旧可以在一定范围内有效工作的融差控制方法。具体分析了融差抓取方法如何运用相同控制量实现不同抓取任务的工作原理,这一原理使得融差抓取方法在面对一大类抓取任务时,不需要知道物体的具体参数,只需要知道这一大类物体的边界条件。进一步分析了融差抓取方法在欠驱动手爪上的适用性,并发现了欠驱动手爪的局限性。实验表明,在控制量设定不变的情况下,依据融差抓取方法,柔性手爪可以抓住且不抓坏宽度范围为5～45 mm的嫩豆腐,且能够成功抓取宽度范围为5～60 mm的硬质长方体;弹簧关节欠驱动手爪可以抓住且不抓坏宽度范围为20～40 mm的嫩豆腐,且能够成功抓取宽度范围为5～60 mm的硬质长方体。这体现了融差抓取方法的通用性和欠驱动手爪在抓取柔性物体时的局限性。最后,展示了柔性手爪使用融差抓取方法在桌面抓取应用中以简单的控制策略成功抓取不同形状、不同材质的物体。这充分说明了融差抓取方法不依赖于精确的对象感知及物体模型,能够简化控制策略。     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Novel soft bending actuator-based power augmentation hand exoskeleton controlled by human intention

This article presents the development of a soft material power augmentation wearable robot using novel bending soft artificial muscles. This soft exoskeleton was developed as a human hand power augmentation system for healthy or partially hand disabled individuals. The proposed prototype serves healthy manual workers by decreasing the muscular effort needed for grasping objects. Furthermore, it is a power augmentation wearable robot for partially hand disabled or post-stroke patients, supporting and augmenting the fingers’ grasping force with minimum muscular effort in most everyday activities. This wearable robot can fit any adult hand size without the need for any mechanical system changes or calibration. Novel bending soft actuators are developed to actuate this power augmentation device. The performance of these actuators has been experimentally assessed. A geometrical kinematic analysis and mathematical output force model have been developed for the novel actuators. The performance of this mathematical model has been proven experimentally with promising results. The control system of this exoskeleton is created by hybridization between cascaded position and force closed-loop intelligent controllers. The cascaded position controller is designed for the bending actuators to follow the fingers in their bending movements. The force controller is developed to control the grasping force augmentation. The operation of the control system with the exoskeleton has been experimentally validated. EMG signals were monitored during the experiments to determine that the proposed exoskeleton system decreased the muscular efforts of the wearer.     

Intelligent control of multi-fingered hands

This paper is concerned with intelligent control for grasping and manipulation of an object by multi-fingered robot hands with rigid or soft hemispheric finger ends that induce rolling contacts with the object. Even in the case of 2D motion like pinching by means of a pair of multi-degrees of freedom robot fingers, there arises an interesting family of Lagrange’s equations of motion with many geometric constraints, which are under-actuated, redundant, and non-holonomic in some sense. Regardless of underactuation of dynamics, it is possible to find a class of sensory feedback signals that realize secure grasp of an object together with control of object orientation. In regard to the secure grasping, a problem of force/torque closure for 2D objects in a dynamic sense plays a crucial role. It is shown that proposed sensory feedback signals satisfying the dynamic force/torque closure can be constructed without knowing object kinematic parameters and location of the mass center. To prove the convergence of motion of the overall fingers–object system under the circumstance of redundancy of joints, new concepts called “stability on a manifold” and “asymptotic stability on a manifold” are introduced. Based on the results found for intelligent control of robotic hands, the last two sections attempt to discuss why human multi-fingered hands can become so dexterous at grasping and object manipulation.     

Motion Planning for Two Robots of an Object Handling System Considering Fast Transition Between Stable States

Fast transition from a stable initial state to a stable handling state is important when multiple mobile robots grasp and transport a bulky and heavy object. In this paper, we present motion planning for two robots of an irregularly shaped object handling system considering fast transition between stable states. A cooperative object handling system consisting of a gripper robot equipped with a gripper and a lifter robot equipped with a lifter was first designed. Then, a strategy to realize fast transition between stable states by using the object handling system designed was proposed. While grasping and lifting an object off the ground, a gripper robot grasps and lifts up the object from one side to provide enough space for a lifter robot to lift the object off the ground cooperatively. Fast transition between stable states is formulated as a constraint optimization problem. The goal is to realize transition from a stable initial state to a stable handling state in a minimal amount of time. Experiments involving two robots and everyday objects were conducted. The two robots cooperatively obtained fast transition between stable states. The results illustrate the validity of the proposed method.     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper

The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping objects. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance. The optimal topology of the gripper structure was obtained by iterative finite element method (FEM) optimization procedure. The main points of this paper are in explanation of a new sensing capability of the gripper for grasping and lifting up the gripping objects. Since the sensor stress depends on weight of the grasping object it is appropriate to establish a prediction model for estimation of the grasping object weight in relation to sensor stress. A soft computing based prediction model was developed. In this study an adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology to conduct prediction of the grasping objects weight. The training and checking data for the ANFIS network were obtained by FEM simulations.     

A soft gripper with variable stiffness inspired by pangolin scales,toothed pneumatic actuator and autonomous controller

Aiming at combining compliant covering and rigid lifting to the object grasping, this paper presents the design principle of a variable stiffness soft gripper and carries out its structural design, gripper fabrication and controller development. The proposed soft finger is composed of a variable stiffness layer and a pneumatic driven layer. The variable stiffness layer is inspired by the pangolins whose scales are flexible in daily activities and become tough when being threatened by predators. A toothed pneumatic actuator is designed to supply power with increased stiffness. The three-finger soft gripper is fabricated by 3D printing and molding of super elastic material. The tests for verifying grasping capability and variable stiffness are implemented. Experimental results show that the gripper can grasp a large variety of objects and achieve enhanced stiffness. The stiffness of the gripper is more than twice higher than the pneumatic gripper without variable stiffness structure. Finally, the control system for autonomous grasping is developed. The control block is divided into the actuation layer, information processing layer and user interface layer. According to the grasping process, the feedback signals in the information processing layer are collected by sensors. A safe grasping assessment is added to the control scheme for changing the gripper stiffness autonomously, which differs from the traditional soft gripper controller. The proposed soft gripper has variable stiffness, enhanced pneumatic input, autonomous control system. Therefore, it has great potential to be applied in the unstructured environment for effective, adaptable and safe object grasping.     

一种基于随动视觉的排爆机器人自主抓取系统

在传统基于固定视觉的排爆机器人抓取系统中,相机视觉易被遮挡且不能保证拍摄清晰度。基于随动视觉技术,提出一种将深度相机置于机械手末端并随机械手运动的排爆机器人自主抓取系统。利用深度相机计算目标物体的三维坐标,采用坐标转换方法将目标物体的位置坐标信息实时转换至机器人全局坐标系,并研究相机坐标系、机器人全局坐标系与末端执行器手爪工具坐标系三者的动态映射关系,实现排爆机器人的自主抓取。实验结果表明,与传统固定视觉方法相比,随动视觉方法可在误差2cm内,使得机器人机械手爪准确到达目标物体所在位置,且当机器人距离目标物体100cm~150cm时,抓取效果最佳。     

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Force control of a robot gripper based on human grasping schemes

This paper describes the force control of a robot gripper that is modeled on the basis of human grasping schemes. In the cases in which fluctuation in load is induced by movement of the object, human beings are able to precisely change the grasping forces according to changes in fingertip forces perpendicular to the grasping direction. The characteristics of strengthening and weakening of forces vary with respect to the safety margin. Here, a model for determining the grasping force of a robot gripper, which depends on the object's acceleration, is described. In this model, unexpected subtle load forces can be compensated by minimal required forces to prevent slip.     

gripper

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.     

基于强化学习的煤矸石分拣机械臂智能控制算法研究

针对传统煤矸石分拣机械臂控制算法如抓取函数法、基于费拉里法的动态目标抓取算法等依赖于精确的环境模型、且控制过程缺乏自适应性,传统深度确定性策略梯度(DDPG)等智能控制算法存在输出动作过大及稀疏奖励容易被淹没等问题,对传统DDPG算法中的神经网络结构和奖励函数进行了改进,提出了一种适合处理六自由度煤矸石分拣机械臂的基于强化学习的改进DDPG算法。煤矸石进入机械臂工作空间后,改进DDPG算法可根据相应传感器返回的煤矸石位置及机械臂状态进行决策,并向相应运动控制器输出一组关节角状态控制量,根据煤矸石位置及关节角状态控制量控制机械臂运动,使机械臂运动到煤矸石附近,实现煤矸石分拣。仿真实验结果表明:改进DDPG算法相较于传统DDPG算法具有无模型通用性强及在与环境交互中可自适应学习抓取姿态的优势,可率先收敛于探索过程中所遇的最大奖励值,利用改进DDPG算法控制的机械臂所学策略泛化性更好、输出的关节角状态控制量更小、煤矸石分拣效率更高。     

Design and Developmental Metrics of a ‘Skin-Like’ Multi-Input Quasi-Compliant Robotic Gripper Sensor Using Tactile Matrix

Slip-resistant robust grasping of objects during remote manipulation remains one of the major open issues in robotics. Finer measurement of tangential force and slippage need to be considered for the task planning and control of robotic gripper in operation. Design and development of such a multi-sensory tactile array is reported in this paper, which is aimed for direct use in an instrumented jaw intelligent robot gripper for potentially hazardous radioactive environments. A new design has been reported in the paper, wherein sensing members of the prototype follow a combination of beam (bending) and truss-type (axial deformation) behavior under external loadings. Various characteristics of the sensor, viz. condition number, static and dynamic stiffness, sensitivity and repeatability have been evaluated, based on the results from field trials of the prototype. Besides the comparatively larger prototype, a miniaturized version of the sensor has also been developed and tested for object grasping in real-time.     

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

基于机器视觉的非特定物体的智能抓取系统的研究

针对传统机械臂局限于按既定流程对固定位姿的特定物体进行机械化抓取,设计了一种基于机器视觉的非特定物体的智能抓取系统;系统通过特定的卷积神经网络对深度相机采集到的图像进行目标定位,并在图像上预测出一个该目标的可靠抓取位置,系统进一步将抓取位置信息反馈给机械臂,机械臂根据该信息完成对目标物体的抓取操作;系统基于机器人操作系统,硬件之间通过机器人操作系统的话题机制传递必要信息;最终经多次实验结果表明,通过改进的快速搜索随机树运动规划算法,桌面型机械臂能够根据神经网络模型反馈的的标记位置对不同位姿的非特定物体进行实时有效的抓取,在一定程度上提高了机械臂的自主能力,弥补了传统机械臂的不足.     

基于卷积神经网络的软硬触觉感知方法研究

智能机器手的应用已经遍布医疗、军工、农业及装配行业等领域.软硬作为物体的重要物理属性之一,对机器手的抓取控制物体有重大影响.在深度学习框架下,基于卷积神经网络提出了用于触觉感知的软硬物体的识别方法.使用薄膜压力传感器采集手指按压软硬物体的数据,建立训练和测试数据集,在Caffe中训练网络,以模拟触觉识别软硬物体.实验结果显示:对软硬物体的识别准确率达94.52%,表明,卷积神经网络对于识别软硬物体有比较好的分类效果.