Tuning of Parameters for Robotic Contouring Based on the Evaluation of Force Deviation

The application of industrial robots with advanced sensor systems in unstructured environments is continuously becoming wider. A widely used type of advanced sensor systems is the force-torque sensor. Force-torque sensors are typically used for applications such as robot grinding, sanding, polishing, and deburring, where a constant force is exerted upon a workpiece. In this research, control parameters for exerting a constant force along a predefined path are evaluated in laboratory conditions. The experimental setup with the contouring force feedback is composed of a Fanuc LRMate six-degree-of-freedom industrial robot with an integrated force-torque sensor. Control parameters of the Contouring function within the Fanuc robot controller are tuned in four contouring experiments. The experiments conducted in this research are: i) flat beam, ii) flat beam with a rigid support, iii) wave shaped compliant plate, and iv) compliant flat plate. During the experiments, contouring parameters were altered in order to collect the feedback on the values of the force to be used for the evaluation of the force deviation. A fitness function for the evaluation of the force deviation and the tuning of the control parameters is presented. The fitness function enables a selection of initial control parameters which minimize the force deviation during the robot contouring process

Extending an industrial root controller : implementation and applications of a fast open sensor interface

An overview is given of the design and implementation of a platform for fast external sensor integration in an industrial robot system called ABB S4CPlus. As an application and motivating example, the implementation of force-controlled grinding and deburring within the AUTOFETT-project is discussed. Experiences from industrial usage of the fully developed prototype confirms the appropriateness of the design choices, thus also confirming the fact that control and software need to be tightly integrated. The new sensor can be used for the prototyping and development of a wide variety of new application

Industrial automation and control in hazardous nuclear environments

textThis report discusses the design and implementation of an automated system for use in geometrically-constrained, hazardous glovebox environments. This system’s purpose is to reduce a hemispherical plutonium pit into smaller pieces that fit inside of a crucible. The size reduction of plutonium pits supports stockpile stewardship efforts by the United States Department of Energy. The automation of this process increases the safety of radiation workers by handling radioactive nuclear material. This decreases glovebox worker dose and exposure to tools, sharps, and fines. This effort examines the hardware and software framework developed to support the use of a Port Deployed Manipulator (PDM) for a contact task. This research effort uses a 7 Degree-of-Freedom (DOF) PDM and a micropunch to reduce hemispherical pit surrogates. Formulation of the material reduction execution algorithm involved addressing a variety of topics related to industrial automation: 1. Collision detection and object recognition based on user-specified parameters. 2. Joint torque monitoring 3. Online motion planning for contact tasks 4. Object-in-hand industrial manufacturing 5. Grasping and handling of nuclear material 6. Software compliance via robust nonlinear control methods A high-bandwidth collision detection algorithm involving joint torque monitoring was developed to increase robot safety during operation. The motion planning algorithm developed for this effort takes variable geometric properties to be used with a range of hemishells. The algorithm’s feasibility was validated on a hardware test bed in a laboratory setting. Hardware cold tests conclude that mechanical compliance is sufficient for task completion. However, software compliance would increase performance, ef- ficiency, and safety during task execution. Two different nonlinear force control laws (feedback linearization and sliding mode control) that minimize object shear forces were developed using a simplified material reduction simulation. It is recommended that glovebox automation research continue to increase worker safety throughout the DOE complex.Mechanical Engineerin

An Intelligent System Approach to the Dynamic Hybrid Robot Control

The objective of this study was to solve the robot dynamic hybrid control problem using intelligent computational processes. In the course of problem- solving, biologically inspired models were used. This was because a robot can be seen as a physical intelligent system which interacts with the real world environment by means of its sensors and actuators. In the robot hybrid control method the neural networks, fuzzy logics and randomization strategies were used. To derive a complete intelligent state-of-the-art hybrid control system, several experiments were conducted in the study. Firstly an algorithm was formulated that can estimate the attracting basin boundary for a stable equilibrium point of a robot's kinematic nonlinear system. From this point the Artificial Neural Networks (ANN) based solution approach was verified for the inverse kinematics solution. Secondly, for the intelligent trajectory generation approach, the segmented tree neural networks for each link (inverse kinematics solution) and the randomness with fuzziness (coping the unstructured environment from the cost function) were used. A one-pass smoothing algorithm was used to generate a practical smooth trajectory path in near real time. Finally, for the hybrid control system the task was decomposed into several individual intelligent control agents, where the task space was split into the position-controlled subspaces, the force-controlled subspaces and the uncertain hyper plane identification subspaces. The problem was considered as a blind-tracking task by a human