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.     

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.     

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.     

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.     

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。     

Intelligent robotic gripper with adaptive grasping force

The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space for handling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile or soft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searched by trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It has too expensive and complex control strategy disadvantages for most of industrial applications. In addition, it can not overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hard features. A communication signal is specified to integrate both robot arm and gripper control kernels for executing the robotic position control and gripper force control functions in sequence. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify the dynamic performance of this distributed control system. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

     

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.     

Optimization of grasping forces in handling of brittle objects

This paper deals with the optimization of grasping brittle objects with a multi-fingered robot hand under general constraints such as finger deformability and object positioning tolerances. First, a general formulation describing hyperstatic grasping is presented. Then an optimization criterion based on the minimization of squeezing forces and torques is introduced. And finally results of numerical simulation for grasping with a special three-fingered gripper are presented.     

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.     

杂乱场景下小物体抓取检测研究

目的 杂乱场景下的物体抓取姿态检测是智能机器人的一项基本技能。尽管六自由度抓取学习取得了进展,但先前的方法在采样和学习中忽略了物体尺寸差异,导致在小物体上抓取表现较差。方法 提出了一种物体掩码辅助采样方法,在所有物体上采样相同的点以平衡抓取分布,解决了采样点分布不均匀问题。此外,学习时采用多尺度学习策略,在物体部分点云上使用多尺度圆柱分组以提升局部几何表示能力,解决了由物体尺度差异导致的学习抓取操作参数困难问题。通过设计一个端到端的抓取网络,嵌入了提出的采样和学习方法,能够有效提升物体抓取检测性能。结果 在大型基准数据集GraspNet-1Billion上进行评估,本文方法取得对比方法中的最优性能,其中在小物体上的抓取指标平均提升了7%,大量的真实机器人实验也表明该方法具有抓取未知物体的良好泛化性能。结论 本文聚焦于小物体上的抓取,提出了一种掩码辅助采样方法嵌入到提出的端到端学习网络中,并引入了多尺度分组学习策略提高物体的局部几何表示,能够有效提升在小尺寸物体上的抓取质量,并在所有物体上的抓取评估结果都超过了对比方法。     

A Neural Tactile Architecture Applied to Real-time Stiffness Estimation for a Large Scale of Robotic Grasping Systems

This paper presents a model for solving the problem of real-time neural estimation of stiffness characteristics for unknown objects. For that, an original neural architecture is proposed for a large scale robotic grasping systems applied for unknown object with unspecified stiffness characteristics. The force acquisition is based on tactile information from force sensors in robotic manipulator. The proposed model has been implemented on a robotic gripper with two parallel fingers and on a one d.o.f. robotic finger with opponent artificial muscles and angular displacements. This self-organized model is inspired of human biological system, and is carried out by means of Topographic Maps and Vector Associative Maps. Experimental results demonstrate the efficiency of this new approach.     

Determining the joints most strained in an underactuated robotic finger by adaptive neuro-fuzzy methodology

The main purpose of this paper is to determine what joints are most strained in the proposed underactuated finger by adaptive neuro-fuzzy methodology. For this, kinetostatic analysis of the finger structure is established with added torsional springs in every single joint. Since the finger’s grasping forces depend on torsional spring stiffness in the joints, it is preferable to determine which joints have the most influence on grasping forces. Hence, the finger joints experiencing the most strain during the grasping process should be determined. It is desirable to select and analyze a subset of joints that are truly relevant or the most influential to finger grasping forces in order to build a finger model with optimal grasping features. This procedure is called variable selection. In this study, variable selection is modeled using the adaptive neuro-fuzzy inference system (ANFIS). Variable selection using the ANFIS network is performed to determine how the springs implemented in the finger joints affect the output grasping forces. This intelligent algorithm is applied using the Matlab environment and the performance is analyzed. The simulation results presented in this paper show the effectiveness of the developed method.     

A novel coupled and self-adaptive under-actuated multi-fingered hand with gear–rack–slider mechanism

Aiming to overcome the serious disadvantages of two kinds of under-actuated fingers: coupled finger and self-adaptive finger, this paper proposed a novel grasping mode, called Coupled and Self-Adaptive (COSA) grasping mode, which includes two stages: first coupled and self-adaptive grasping. A 2-joint COSA finger with a double gear–rack–slider mechanism (called COSA-GRS finger), is developed based on the COSA grasping mode: at the beginning, the 2-joint finger bends with coupled mode, two joints of the finger rotate simultaneously with a fixed ratio until the proximal phalanx touches the grasped object, then the finger will automatically decouple and rotate with self-adaptive mode, the distal phalanx quickly rotates until it touches the object. The new finger unit has the advantages of coupled fingers and self-adaptive fingers. The finger is not only able to rotate all joints simultaneously to pre-shape before grasping objects, but also able to self-adapt different sizes and shapes of objects. Using the same mechanism as the 2-joint finger, a 3-joint COSA finger is designed. Force analyses and a structure optimization rule of the new finger are given and discussed. The simulation results show that the finger unit is effective: it can successfully realize coupling and decoupling and it can stably grasp objects. An under-actuated humanoid robot hand is developed, called the COSA-GRS Hand. The hand has 5 fingers, 15 joints and 6 motors. All fingers of the hand are COSA fingers. The hand is more similar to human hand in appearance and actions, able to grasp different objects more dexterously and stably than traditional coupled or self-adaptive under-actuated hands.     

Grasping and in-hand manipulation: experiments with a reconfigurable gripper

This paper addresses the problem of grasping and manipulating three-dimensional objects with a reconfigurable gripper equipped with two parallel plates whose distance can be adjusted by a computer-controlled actuator. The bottom plate is a bare plane and the top one carries a rectangular grid of actuated pins that can translate in discrete increments under computer control. We propose to use this gripper to immobilize objects through frictionless contacts with three of the pins and the bottom plate, and to manipulate an object within a grasp by planning the sequence of pin configurations that will bring this object to a desired position and orientation. A detailed analysis of the problem geometry in configuration space was used in a previous paper to devise simple and efficient algorithms for grasp and manipulation planning. We have constructed a prototype of the gripper and this paper presents our experiments.     

Synthesizing two-fingered grippers for positioning and identifyingobjects

Most industrial grippers now in use are two-fingered. Among them the parallel-jaw gripper is the simplest. It can partially remove the pose uncertainty of an object through grasping, such as the orientation uncertainty. This paper addresses a new type of grippers with the finger configuration of four circles instead of two parallel lines. It has a number of important advantages. Especially, it achieves form-closure and confines the object to a locally unique pose, so as to remove the pose uncertainty completely. It allows the gripped object to reach this pose freely without loss of required friction in the direction perpendicular to the grasping plane. More information can be acquired for identifying the object and its grasp mode. As a result the identification can be performed at one grasp. The key parameter of a symmetric four-pin gripper is the distance (span) between two pin centers on each finger, which depends upon the object shape and impacts the closure property, Based on a new approach to the grasp geometry, selection and limitations of the span are illustrated.     

The influence of the radial basis function training strategy on the performance of a neural gripper

In this article, a neural network-based grasping system that is able to collect objects of arbitrary shape is introduced. The grasping process is split into three functional blocks: image acquisition and processing, contact point estimation, and contact force determination. The paper focuses on the second block, which contains two neural networks. A competitive Hopfield neural network first determines an approximate polygon for an object outline. These polygon edges are the input for a supervised neural network model [radial basis function (RBF) or multilayer perceptions], which then defines the contact points. Tests were conducted with objects of different shapes, and experimental results suggest that the performance of the neural gripper and its learning rate are significantly influenced by the choice of supervised training model and RBF learning algorithm.     

Digital twin-enabled grasp outcomes assessment for unknown objects using visual-tactile fusion perception

Humans can instinctively predict whether a given grasp will be successful through visual and rich haptic feedback. Towards the next generation of smart robotic manufacturing, robots must be equipped with similar capabilities to cope with grasping unknown objects in unstructured environments. However, most existing data-driven methods take global visual images and tactile readings from the real-world system as input, making them incapable of predicting the grasp outcomes for cluttered objects or generating large-scale datasets. First, this paper proposes a visual-tactile fusion method to predict the results of grasping cluttered objects, which is the most common scenario for grasping applications. Concretely, the multimodal fusion network (MMFN) uses the local point cloud within the gripper as the visual signal input, while the tactile signal input is the images provided by two high-resolution tactile sensors. Second, collecting data in the real world is high-cost and time-consuming. Therefore, this paper proposes a digital twin-enabled robotic grasping system to collect large-scale multimodal datasets and investigates how to apply domain randomization and domain adaptation to bridge the sim-to-real transfer gap. Finally, extensive validation experiments are conducted in physical and virtual environments. The experimental results demonstrate the effectiveness of the proposed method in assessing grasp stability for cluttered objects and performing zero-shot sim-to-real policy transfer on the real robot with the aid of the proposed migration strategy.     

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.