User Needs, Benefits, and Integration of Robotic Systems in a Space Station Laboratory

The methodology, results and conclusions of all tasks of the User Needs, Benefits, and Integration Study (UNBIS) of Robotic Systems in a Space Station Laboratory are summarized. Study goals included the determination of user requirements for robotics within the Space Station, United States Laboratory. In Task 1, three experiments were selected to determine user needs and to allow detailed investigation of microgravity requirements. In Task 2, a NASTRAN analysis of Space Station response to robotic disturbances, and acceleration measurement of a standard industrial robot (Intelledex Model 660) resulted in selection of two ranges of microgravity manipulation: Level 1 (10-3 to 10-5 G at greater than 1 Hz) and Level 2 (less than equal 10-6 G at 0.1 Hz). This task included an evaluation of microstepping methods for controlling stepper motors and concluded that an industrial robot actuator can perform milli-G motion without modification. Relative merits of end-effectors and manipulators were studied in Task 3 in order to determine their ability to perform a range of tasks related to the three microgravity experiments. An Effectivity Rating was established for evaluating these robotic system capabilities. Preliminary interface requirements for an orbital flight demonstration were determined in Task 4. Task 5 assessed the impact of robotics

User needs, benefits and integration of robotic systems in a space station laboratory

The methodology, results and conclusions of the User Needs, Benefits, and Integration Study (UNBIS) of Robotic Systems in the Space Station Microgravity and Materials Processing Facility are summarized. Study goals include the determination of user requirements for robotics within the Space Station, United States Laboratory. Three experiments were selected to determine user needs and to allow detailed investigation of microgravity requirements. A NASTRAN analysis of Space Station response to robotic disturbances, and acceleration measurement of a standard industrial robot (Intelledex Model 660) resulted in selection of two ranges of low gravity manipulation: Level 1 (10-3 to 10-5 G at greater than 1 Hz.) and Level 2 (less than = 10-6 G at 0.1 Hz). This included an evaluation of microstepping methods for controlling stepper motors and concluded that an industrial robot actuator can perform milli-G motion without modification. Relative merits of end-effectors and manipulators were studied in order to determine their ability to perform a range of tasks related to the three low gravity experiments. An Effectivity Rating was established for evaluating these robotic system capabilities. Preliminary interface requirements were determined such that definition of requirements for an orbital flight demonstration experiment may be established

CONTROLLING OF AN INDUSTRIAL ROBOTIC ARM

Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed

THE DEVELOPMENT OF A MECHATRONICS AND MATERIAL HANDLING COURSE: LABORATORY EXPERIMENTS AND PROJECTS

Mechatronic systems integrate technologies from a variety of engineering disciplines to create solutions to challenging industrial problems. The material handling industry utilizes mechatronics to move, track, and manipulate items in factories and distribution centers. Material handling systems, because of their use of programmable logic controllers (PLC), PLC networks, industrial robotics, and other mechatronic elements, are a natural choice for a college instructional environment. This thesis offers insight and guidance for mechatronic activities introduced in a laboratory setting. A series of eight laboratory experiments have been created to introduce PLCs, robotics, electric circuits, and data acquisition fundamentals. In-depth case studies synthesize the technologies and interpersonal skills together to create a flexible material handling system. Student response to the course and laboratory material was exceptional. A pre and post course questionnaire was administered which covered topics such as teamwork, human factors, business methods, and various engineering related questions. Quantitative scores resulting from these questionnaires showed a marked improvement by students, especially in regards to technical/engineering questions. The responses from students generally indicated an excitement about course material and a thorough understanding of the various syllabus topics. In this thesis, the multi-disciplinary mechatronics (and material handling systems) laboratory will be presented. An in-depth examination of each laboratory will be offered as well as the discussion of two material handling case studies. The Appendixes contain the PLC and robot code for a order fulfillment case study

Dynamic behavior analysis for a six axis industrial machining robot

The six axis robots are widely used in automotive industry for their good repeatability (as defined in the ISO92983) (painting, welding, mastic deposition, handling etc.). In the aerospace industry, robot starts to be used for complex applications such as drilling, riveting, fiber placement, NDT, etc. Given the positioning performance of serial robots, precision applications require usually external measurement device with complexes calibration procedure in order to reach the precision needed. New applications in the machining field of composite material (aerospace, naval, or wind turbine for example) intend to use off line programming of serial robot without the use of calibration or external measurement device. For those applications, the position, orientation and path trajectory precision of the tool center point of the robot are needed to generate the machining operation. This article presents the different conditions that currently limit the development of robots in robotic machining applications. We analyze the dynamical behavior of a robot KUKA KR240-2 (located at the University of Bordeaux 1) equipped with a HSM Spindle (42000 rpm, 18kW). This analysis is done in three stages. The first step is determining the self-excited frequencies of the robot structure for three different configurations of work. The second phase aims to analyze the dynamical vibration of the structure as the spindle is activated without cutting. The third stage consists of vibration analysis during a milling operation

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA)

Rakshak - A Rescue Bot

Robotics is the field of designing and innovation that assumes a vital part in making our lives simpler to perform different undertakings. The primary point of the venture is to outline and build up, a sensor based mechanical arm which is controlled by human hand. The venture manages controlling a servo mechanical arm with the assistance of movement detecting innovation by Flex sensor and 3-hub accelerometer. The pick and place operation of the mechanical arm can be controlled utilizing Arduino. This framework can be utilized as a part of enterprises, dangerous conditions, biomedical surgeries and different applications

Learning Control of Robotic Arm Using Deep Q-Neural Network

Enabling robotic systems for autonomous actions such as driverless systems, is a very complex task in real-world scenarios due to uncertainties. Machine learning capabilities have been quickly making their way into autonomous systems and industrial robotics technology. They found many applications in every sector, including autonomous vehicles, humanoid robots, drones and many more. In this research we will be implementing artificial intelligence in robotic arm to be able to solve a complex balancing control problem from scratch, without any feedback loop and using state of the art deep reinforcement learning algorithm named DQN. The benchmark problem that is considered as case study, is balancing an inverted pendulum upward using a six-degrees freedom robot arm. Very simple form of this problem has been solved recently using machine learning however under this thesis we made a very complex system of inverted pendulum and implemented in Robot Operating System (ROS) which is very realistic simulation environment. We have not only succeeded to control the pendulum but also added turbulences on the learned model to study its robustness. We observed how the initial learned model is unstable at the presence of turbulence and how random turbulences helps the system to transform to a more robust model. We have also used the robust model in different environment and showed how the model adopt itself with the new physical properties. Using orientation sensor on the tip of the inverted pendulum to get angular velocity, simulation in ROS and having inverted pendulum on ball joint are few highlighted novelties in this thesis in compare previous publications