Intelligent Robotic Manipulator Research Articles

A Multi-Posture Grasping Manipulator Actuated by Shape Memory Alloy with Different Functional Modules.

Currently, multi-posture robots have complex grasping robotic manipulators with low power density, making it difficult to miniaturize and integrate. In this paper, a multi-posture grasping manipulator actuated by shape memory alloy with different functional modules is presented. It is composed of deflection, translation, rotation and grasping modules. Based on a D-H parameter method, the end motion trajectory model is established and the end motion space is drawn. Finally, the grasping experiment of a light circular object is carried out to verify the validity of the multi-posture grasping function of the multi-module combination manipulator, which provides a choice for future intelligent robot manipulators.

Toward Human-Like Grasp: Functional Grasp by Dexterous Robotic Hand Via Object-Hand Semantic Representation.

Intelligent robotic manipulation is a challenging study of machine intelligence. Although many dexterous robotic hands have been designed to assist or replace human hands in executing various tasks, how to teach them to perform dexterous operations like human hands is still a challenge. This motivates us to conduct an in-depth analysis of human behavior in manipulating objects and propose an object-hand manipulation representation. This representation provides an intuitive and clear semantic indication of how the dexterous hand should touch and manipulate an object based on the object's own functional areas. At the same time, we propose a functional grasp synthesis framework, which does not require real grasp label supervision, but relies on the guidance of our object-hand manipulation representation. In addition, in order to obtain better functional grasp synthesis results, we propose a network pre-training method that can make full use of easily obtained stable grasp data, and a network training strategy to coordinate the loss functions. We conduct object manipulation experiments on a real robot platform, and evaluate the performance and generalization of our object-hand manipulation representation and grasp synthesis framework. The project website is: https://github.com/zhutq-github/Toward-Human-Like-Grasp-V2-.

Integration of Robotic Vision and Automatic Tool Changer Based on Sequential Motion Primitive for Performing Assembly Tasks

Studies on intelligent robotic manipulation systems have typically focused on the programming efficiency, adaptive control of robotic arms, motion planning of robotic arms, and action diversity of grippers. In this study, a decision tree and visual recognition incorporate into a robotic arm to help it learn complex tasks. This study employed a task tree for the automatic planning of complex tasks, in which a decision-making model was used to generate complex task sets from a pre-built motion dataset in real-time performance. Moreover, the model can analyze the rationality of model steps, introduce new tasks, and perform object analysis. This work applies a support vector machine to identify the state of an object. The model selects a suitable gripper with a rapid gripper switch process by considering the characteristics of the targeting object. This study demonstrated the effectiveness of the proposed approach with suitable intelligence through the assembly task.

Singular Configuration Analysis and Singularity Avoidance with Application in an Intelligent Robotic Manipulator

Recently, robotic sensor systems have gained more attention annually in complex system sense strategies. The robotic sensors sense the information from itself and the environment, and fuse information for the use of perception, decision, planning, and control. As an important supplement to traditional industrial robots, co-bots (short for co-working robots) play an increasingly vital role in helping small and medium-sized enterprises realize intelligent manufacturing. They have high flexibility and safety so that they can assist humans to complete highly repetitive and high-precision work. In order to maintain robot safe operation in the increasing complex working environment and human–computer intelligent interactive control, this paper is concerned with the problem of applicant accuracy analysis and singularity avoidance for co-bots. Based on the dynamic model with load and torque sensors, which is used to detect the external force at the end of the robot, this paper systematically analyzes the causes of singularity phenomenon in the robot motion control. The inverse solution is obtained by analytical method and numerical method, respectively. In order to ensure the smooth and safe operation in the whole workspace, it is necessary for a robot to avoid singularity. Singularity avoidance schemes are utilized for different control tasks, including point-to-point control and continuous path control. Corresponding simulation experiments are designed to verify the effectiveness of different evasion schemes, in which the advantages and disadvantages are compared and analyzed.

Robotic Manipulation under Harsh Conditions Using Self‐Healing Silk‐Based Iontronics (Adv. Sci. 2/2022)

Silk-Based Iontronics In article number 2102596, Tiger H. Tao and co-workers report a unique silk-based iontronic composite to simultaneously achieve high conductivity, self-healing, strong stretchability, high strain sensitivity and transparency, and universal adhesiveness across a broad working temperature range. Those merits facilitate the implementation of damage-resilient intelligent robotic manipulation.

A Suite of Robotic Solutions for Nuclear Waste Decommissioning

Dealing safely with nuclear waste is an imperative for the nuclear industry. Increasingly, robots are being developed to carry out complex tasks such as perceiving, grasping, cutting, and manipulating waste. Radioactive material can be sorted, and either stored safely or disposed of appropriately, entirely through the actions of remotely controlled robots. Radiological characterisation is also critical during the decommissioning of nuclear facilities. It involves the detection and labelling of radiation levels, waste materials, and contaminants, as well as determining other related parameters (e.g., thermal and chemical), with the data visualised as 3D scene models. This paper overviews work by researchers at the QMUL Centre for Advanced Robotics (ARQ), a partner in the UK EPSRC National Centre for Nuclear Robotics (NCNR), a consortium working on the development of radiation-hardened robots fit to handle nuclear waste. Three areas of nuclear-related research are covered here: human–robot interfaces for remote operations, sensor delivery, and intelligent robotic manipulation.

Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.

Texture-less object detection and 6D pose estimation in RGB-D images

We present a cascade-like texture-less object detection and 6D pose estimation method exploiting both depth and color information from the RGB-D sensor. This is accomplished through both an offline and online phase. During the offline phase, a set of rendered templates is created from uniformly distributed sampling viewpoints obtained by employing the model of electrostatic charge distribution. During the online phase, our method converts sliding windows to scale-invariant RGB-D patches and employs a hash voting-based hypothesis generation scheme to compute a rough 6D pose hypothesis. Particle swarm optimization is employed to refine the 6D pose of the target object. We evaluate the algorithm against three datasets with the result that this method achieves high precision and good performance under various conditions. It was also applied to augmented reality and intelligent robotic manipulation yielding robust detection results.

Design and Implementation of an Intelligent Robotic Manipulator using LabVIEW for Industrial Applications

The robotic manipulator plays a dominant role in the domain of process industries based applications such as pick and drop, quality based sorting, etc. With the advent of these intelligent systems, the overall performance has been grown at a quite fast rate. This system has been mostly developed with the help of various hardware and software modules with the help of graphical coding language i.e. LabVIEW by National Instruments or MatLAB by Mathworks. This virtual intelligent system basically consists of a robotic manipulator, vision based systems, AI based processors i.e. a microcontroller, conveyor belts, etc. In this system, the degree of freedom (DOF) i.e. the movement of the manipulator is quite important which depends upon the nature of the application. For this intelligent system, various algorithms based approaches are implemented so as to execute the task i.e. image processing, coordinate determination, movement of the end effectors using various servo as well as DC motors, etc. In this present work, author has put forward a high performance automated intelligent system that provides a precise output with desired level of accuracy. With such characteristics, this virtual intelligent system can be utilized in various industrial applications where the quality is expected at a high rate with other dominant parameters. The simulation work of the system has been carried out with LabVIEW 2015 edition along with other hardware modules with desired results.

Physics-based simulation for manual robot guidance—An eRobotics approach

Manual robot guidance is an intuitive approach to teach robots with human's skills in the loop. It is particularly useful to manufacturers because of its high flexibility and low programming effort. However, manual robot guidance requires compliance control that is generally not available in position-controlled industrial robots. We address this issue from a simulation-driven approach. We systematically capture the interactive dynamic behavior of intelligent robot manipulators within physics-based virtual testbeds, regardless of the type of application. On this basis, we develop structures to equip and employ simulated robots with motion control capabilities that include soft physical interaction control driven in real-time with real external guidance forces. We then transfer the virtual compliant behavior of the simulated robots to their physical counterparts to enable manual guidance. The simulator provides assistance to operators through timely and insightful robot monitoring, as well as meaningful performance indexes. The testbed allows us to swiftly assess guidance within numerous interaction scenarios. Experimental case studies illustrate the practical usefulness of the symbiotic transition between 3D simulation and reality, as pursued by the eRobotics framework to address challenging issues in industrial automation.

Visual perception for the 3D recognition of geometric pieces in robotic manipulation

During grasping and intelligent robotic manipulation tasks, the camera position relative to the scene changes dramatically because the robot is moving to adapt its path and correctly grasp objects. This is because the camera is mounted at the robot effector. For this reason, in this type of environment, a visual recognition system must be implemented to recognize and “automatically and autonomously” obtain the positions of objects in the scene. Furthermore, in industrial environments, all objects that are manipulated by robots are made of the same material and cannot be differentiated by features such as texture or color. In this work, first, a study and analysis of 3D recognition descriptors has been completed for application in these environments. Second, a visual recognition system designed from specific distributed client-server architecture has been proposed to be applied in the recognition process of industrial objects without these appearance features. Our system has been implemented to overcome problems of recognition when the objects can only be recognized by geometric shape and the simplicity of shapes could create ambiguity. Finally, some real tests are performed and illustrated to verify the satisfactory performance of the proposed system.

Admittance Control of the Intelligent Assistive Robotic Manipulator for Individuals with Duchenne Muscular Dystrophy: A Proof-of- Concept Design

Admittance Control of the Intelligent Assistive Robotic Manipulator for Individuals with Duchenne Muscular Dystrophy: A Proof-of- Concept Design

System Design and Implementation of UCF-MANUS—An Intelligent Assistive Robotic Manipulator

This paper reports on the system design for integrating the various processes needed for end-to-end implementation of a smart assistive robotic manipulator. Specifically, progress is reported in the empowerment of the UCF-MANUS system with a suite of sensory, computational, and multimodal interface capabilities so that its autonomy can be made accessible to users with a wide range of disabilities. Laboratory experiments are reported to demonstrate the ability of the system prototype to successfully and efficiently complete object retrieval tasks. Benchmarking of the impact of the various interface modalities on user performance is performed via empirical studies with healthy subjects operating the robot in a simulated instrumental activities of daily living tasks setup. It is seen through a analysis of the collected quantitative data that the prototype is interface neutral and shows robustness to variations in the tasks and the environment. It is also seen that the prototype autonomous system is quantitatively superior to Cartesian control for all tested tasks under a “number of commands” metric, however, under a “time to task completion” metric, the system is seen to be superior for “hard” tasks but not for “easy” tasks.

Motion Segmentation and Control Design for UCF-MANUS—An Intelligent Assistive Robotic Manipulator

In this paper, we document the progress in the design of a motion segmentation and control strategy for a smart assistive robot arm that can provide assistance during activities of daily living to the elderly and/or users with disabilities. Interaction with the environment is made challenging by the kinematic uncertainty in the robot, imperfect sensor calibration as well as the fact that most activities of daily living are generally required to be performed in unstructured environments. The motion control strategy exploits visual and force feedback from sensors in the robot’s hand to provide the basis for efficient interaction with the unstructured environment. Through experimental studies with a variety of objects of daily life in natural environments, an anthropomorphic-like approach was found to be the most suitable for reliable and speedy object retrieval. Specifically, gross reaching/docking motions of the robot arm using proprioception are followed by fine alignment of the hand through visual feedback and eventually grasping based on haptic feedback. Experimental results using a wheelchair mounted robotic arm are presented to demonstrate the efficacy of the proposed algorithms.

Evolutionary collision-free optimal trajectory planning for intelligent robots

This paper presents optimization procedures based on evolutionary algorithms such as the elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for solving the trajectory planning problem of intelligent robot manipulators with the prevalence of fixed, moving, and oscillating obstacles. The aim is the minimization of a combined objective function, with the constraints being actuator constraints, joint limits, and the obstacle avoidance constraint by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a non-linear constrained optimization problem with six objective functions, 31 constraints, and 42 variables. The combined objective function is a weighted balance of transfer time, the mean average of actuator efforts and power, penalty for collision-free motion, singularity avoidance, joint jerks, and joint accelerations. The obstacles are present in the workspace of the robot. The distance between potentially colliding parts is expressed as obstacle avoidance. Further, the motion is represented using translational and rotational matrices. The proposed optimization techniques are explained by applying them to an industrial robot (PUMA 560 robot). Also, the results obtained from NSGA-II and DE are compared and analyzed. This is the first research work which considers all the decision criteria for the trajectory planning of industrial robots with obstacle avoidance. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed DE algorithm.

Tactile sensing in intelligent robotic manipulation – a review

PurposeWhen designing hardware and algorithms for robotic manipulation and grasping, sensory information is typically needed to control the grasping process. This paper presents an overview of the major grasping and manipulation approaches and the more common hardware used to obtain the necessary sensory information.Design/methodology/approachThis paper presents an overview of tactile sensing in intelligent robotic manipulation. The history, the common issues, and applications are reviewed. Sensor performance is briefly discussed and compared to the human tactile sense. Advantages and disadvantages of the most common sensor approaches are discussed. Some examples are given of sensors that are widely available as of today. Eventually, some examples of the state‐of‐the‐art in tactile sensing application are presented.FindingsAlthough many sensor technologies and strong theoretical models have been developed, there is still much left to be done in intelligent grasping and manipulation. This is partly due to the youth of the field and the complex nature of safe control in uncertain environments. Even though there are impressive results when it comes to specific examples of advanced manipulation, there seems to be room for great improvements of hardware and especially algorithms when it comes to more generic everyday domestic tasks.Originality/valueThis paper presents a review of sensor hardware while also giving a glimpse of the major topics in grasping and manipulation. While better hardware of course is desirable, the major challenges seem to lie in the development and application of grasping and manipulation algorithms.

Real-time integration of sensing, planning and control in robotic work-cells

This paper addresses real-time planning and control for robot manipulators in a re-configurable work-cell or an unknown workspace. The considered tasks in robotic work-cell are (1) to catch a moving object by using vision-guidance system, and (2) to follow a path on an unknown surface. These two tasks are commonly performed in the applications of material handling and processing. In order to efficiently and successfully accomplish the tasks, novel strategies for integrating real-time planning and control are proposed to improve agility and reconfigurability of robotic systems and to deal with the uncertainties in the environment by means of multi-sensor fusion. In the proposed schemes, a novel calibration-free stereo vision algorithm and a new hybrid position/force control strategy are utilized. In order to achieve intelligent robot manipulation in an unstructured workspace and reconfigurable work-cell, complementary sensory data from different types of sensors, such as force/torque, encoders and visual sensors, are integrated simultaneously during robot constrained motion.

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.

Reflexive sonar-based navigation of intelligent robot manipulators in dynamic environments using fuzzy control

This paper presents intelligent robot motion planning based on fuzzy rules for the idea of artificial potential fields using analytic harmonic functions. The purpose of the fuzzy controller is to generate commands for the servo-systems of the robot so it can autonomously find its way to its goal, while reacting in real-time to unexpected events by using a specific active range finding device, i.e. an ultrasonic sensor. Sonar range data is used to build a description of the robot's surroundings. Range measurements are integrated using a robust method, which can cope with uncertainties and errors in the given data. The proposed approach is simple, computationally fast and applies to whole-arm collision avoidance for n d.o.f. arm manipulators. The stability of the overall closed-loop system composed by the nonlinear robot dynamics and the fuzzy controller is guaranteed by Lyapunov theory. Simulation results are provided to validate the theoretical concepts and a comparative analysis demonstrates the benefits of the proposed obstacle avoidance algorithm.

Real-time planning and control for robot manipulator in unknown workspace

This paper addresses real-time planning and control for robot manipulator in unknown workspace. The task under our consideration is to control a robot to follow a path on an unknown surface. In order to successfully accomplish the task, a novel strategy for integrating real-time planning and control is proposed to deal with the uncertainties in the environment by means of multisensor fusion. In the proposed scheme, a new planner is designed to complete the path-following task without apriori knowledge of the path, while the hybrid position/force control strategy is utilized to maintain the contact between the robot and the unknown surface. In this paper, to achieve intelligent robot manipulation in an unstructured workspace, complementary sensory data from force-torque and visual sensors is integrated simultaneously during robot constrained motion.