Smart Grasping using Laser and Tactile Array Sensors for UCF-MANUS- An Intelligent Assistive Robotic Manipulator

This thesis presents three improvements in the UCF MANUS Assistive Robotic Manipulator\u27s grasping abilities. Firstly, the robot can now grasp objects that are deformable, heavy and have uneven contact surfaces without undergoing slippage during robotic operations, e.g. paper cup, filled water bottle. This is achieved by installing a high precision non-contacting Laser sensor1 that runs with an algorithm that processes raw-input data from the sensor, registers smallest variation in the relative position of the object with respect to the gripper. Secondly, the robot can grasp objects that are as light and small as single cereal grain without deforming it. To achieve this a MEMS Barometer based tactile sensor array device that can measure force that are as small as 1 gram equivalent is embedded into the gripper to enhance pressure sensing capabilities. Thirdly, the robot gripper gloves are designed aesthetically and conveniently to accommodate existing and newly added sensors using a 3D printing technology that uses light weight ABS plastic as a fabrication material. The newly designed system was experimented and found that a high degree of adaptability for different kinds of objects can be attained with a better performance than the previous system

Active compliance control strategies for multifingered robot hand

Safety issues have to be enhanced when the robot hand is grasping objects of different shapes, sizes and stiffness. The inability to control the grasping force and finger stiffness can lead to unsafe grasping environment. Although many researches have been conducted to resolve the grasping issues, particularly for the object with different shape, size and stiffness, the grasping control still requires further improvement. Hence, the primary aim of this work is to assess and improve the safety of the robot hand. One of the methods that allows a safe grasping is by employing an active compliance control via the force and impedance control. The implementation of force control considers the proportional–integral–derivative (PID) controller. Meanwhile, the implementation of impedance control employs the integral slidingmode controller (ISMC) and adaptive controller. A series of experiments and simulations is used to demonstrate the fundamental principles of robot grasping. Objects with different shape, size and stiffness are tested using a 3-Finger Adaptive Robot Gripper. The work introduces the Modbus remote terminal unit [RTU] protocol, a low-cost force sensor and the Arduino IO Package for a real-time hardware setup. It is found that, the results of the force control via PID controller are feasible to maintain the grasped object at certain positions, depending on the desired grasping force (i.e., 1N and 8N). Meanwhile, the implementation of impedance control via ISMC and adaptive controller yields multiple stiffness levels for the robot fingers and able to reduce collision between the fingers and the object. However, it was found that the adaptive controller produces better impedance control results as compared to the ISMC, with a 33% efficiency improvement. This work lays important foundations for long-term related research, particularly in the field of active compliance control that can be beneficial to human–robot interaction (HRI)

Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands

Nonlinear Control Synthesis for Facilitation of Human-Robot Interaction

Human-robot interaction is an area of interest that is becoming increasingly important in robotics research. Nonlinear control design techniques allow researchers to guarantee stability, performance, as well as safety, especially in cases involving physical human-robot interaction (PHRI). In this dissertation, we will propose two different nonlinear controllers and detail the design of an assistive robotic system to facilitate human-robot interaction. In Chapter 2, to facilitate physical human-robot interaction, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical interaction during activities such as hair-grooming, scratching, face-sponging, etc. Specifically, we propose a hybrid force/velocity/attitude control for our physical human-robot interaction system which is based on measurements from a force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov based stability analysis is provided to prove both convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid force/velocity/attitude controller in the presence of dynamic uncertainties as well as safety compliance of human-robot interactions for a redundant robot manipulator. Chapter 3 presents the design, analysis, and experimental implementation of an adaptive control enabled intelligent algorithm to facilitate 1-click grasping of novel objects by a robotic gripper since one of the most common types of tasks for an assistive robot is pick and place/object retrieval tasks. But there are a variety of objects in our daily life all of which need different optimal force to grasp them. This algorithm facilitates automated grasping force adjustment. The use of object-geometry free modeling coupled with utilization of interaction force and slip velocity measurements allows for the design of an adaptive backstepping controller that is shown to be asymptotically stable via a Lyapunov-based analysis. Experiments with multiple objects using a prototype gripper with embedded sensing show that the proposed scheme is able to effectively immobilize novel objects within the gripper fingers. Furthermore, it is seen that the adaptation allows for close estimation of the minimum grasp force required for safe grasping which results in minimal deformation of the grasped object. In Chapter 4, we present the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on usability testing for the system, several features were implemented in the interface that could reduce the complexity of the human-robot interaction while also compensating for the deficits in different human factors, such as Working Memory, Response Inhibition, Processing Speed; , Depth Perception, Spatial Ability, Contrast Sensitivity. For the controller part, we designed several new features to provide the user has a less complex and safer interaction with the robot, such as \u27One-click mode\u27, \u27Move suggestion mode\u27 and \u27Gripper Control Assistant\u27. As for the adaptive interface design, we designed and implemented compensators such as \u27Contrast Enhancement\u27, \u27Object Proximity Velocity Reduction\u27 and \u27Orientation Indicator\u27

Methods and Sensors for Slip Detection in Robotics: A Survey

The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector

Design and Development of Sensor Integrated Robotic Hand

Most of the automated systems using robots as agents do use few sensors according to the need. However, there are situations where the tasks carried out by the end-effector, or for that matter by the robot hand needs multiple sensors. The hand, to make the best use of these sensors, and behave autonomously, requires a set of appropriate types of sensors which could be integrated in proper manners. The present research work aims at developing a sensor integrated robot hand that can collect information related to the assigned tasks, assimilate there correctly and then do task action as appropriate. The process of development involves selection of sensors of right types and of right specification, locating then at proper places in the hand, checking their functionality individually and calibrating them for the envisaged process. Since the sensors need to be integrated so that they perform in the desired manner collectively, an integration platform is created using NI PXIe-1082. A set of algorithm is developed for achieving the integrated model. The entire process is first modelled and simulated off line for possible modification in order to ensure that all the sensors do contribute towards the autonomy of the hand for desired activity. This work also involves design of a two-fingered gripper. The design is made in such a way that it is capable of carrying out the desired tasks and can accommodate all the sensors within its fold. The developed sensor integrated hand has been put to work and its performance test has been carried out. This hand can be very useful for part assembly work in industries for any shape of part with a limit on the size of the part in mind. The broad aim is to design, model simulate and develop an advanced robotic hand. Sensors for pick up contacts pressure, force, torque, position, surface profile shape using suitable sensing elements in a robot hand are to be introduced. The hand is a complex structure with large number of degrees of freedom and has multiple sensing capabilities apart from the associated sensing assistance from other organs. The present work is envisaged to add multiple sensors to a two-fingered robotic hand having motion capabilities and constraints similar to the human hand. There has been a good amount of research and development in this field during the last two decades a lot remains to be explored and achieved. The objective of the proposed work is to design, simulate and develop a sensor integrated robotic hand. Its potential applications can be proposed for industrial environments and in healthcare field. The industrial applications include electronic assembly tasks, lighter inspection tasks, etc. Application in healthcare could be in the areas of rehabilitation and assistive techniques. The work also aims to establish the requirement of the robotic hand for the target application areas, to identify the suitable kinds and model of sensors that can be integrated on hand control system. Functioning of motors in the robotic hand and integration of appropriate sensors for the desired motion is explained for the control of the various elements of the hand. Additional sensors, capable of collecting external information and information about the object for manipulation is explored. Processes are designed using various software and hardware tools such as mathematical computation MATLAB, OpenCV library and LabVIEW 2013 DAQ system as applicable, validated theoretically and finally implemented to develop an intelligent robotic hand. The multiple smart sensors are installed on a standard six degree-of-freedom industrial robot KAWASAKI RS06L articulated manipulator, with the two-finger pneumatic SHUNK robotic hand or designed prototype and robot control programs are integrated in such a manner that allows easy application of grasping in an industrial pick-and-place operation where the characteristics of the object can vary or are unknown. The effectiveness of the actual recommended structure is usually proven simply by experiments using calibration involving sensors and manipulator. The dissertation concludes with a summary of the contribution and the scope of further work