A model-based residual approach for human-robot collaboration during manual polishing operations

A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable. Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our recently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor

Intelligent active force control of a three-link manipulator using fuzzy logic

The paper presents a novel approach to estimate the inertia matrix of a robot arm using a fuzzy logic (FL) mechanism in order to trigger the active force control (AFC) strategy. A comprehensive study is performed on a rigid three-link manipulator subjected to a number of external disturbances. The robustness and effectiveness of the proposed control scheme are investigated considering the trajectory track performance of the robotic arm taking into account the application of external disturbances and that the arm is commanded to describe a reference trajectory given a number of initial and operating conditions. The results show that the FL mechanism used in the study successfully computes appropriate estimated inertia matrix value to execute the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbances

Parametric motion control of robotic arms: A biologically based approach using neural networks

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements

Dynamics, control and sensor issues pertinent to robotic hands for the EVA retriever system

Basic dynamics, sensor, control, and related artificial intelligence issues pertinent to smart robotic hands for the Extra Vehicular Activity (EVA) Retriever system are summarized and discussed. These smart hands are to be used as end effectors on arms attached to manned maneuvering units (MMU). The Retriever robotic systems comprised of MMU, arm and smart hands, are being developed to aid crewmen in the performance of routine EVA tasks including tool and object retrieval. The ultimate goal is to enhance the effectiveness of EVA crewmen

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Experimental study of trajectory planning and control of a high precision robot manipulator

The kinematic and trajectory planning is presented for a 6 DOF end-effector whose design was based on the Stewart Platform mechanism. The end-effector was used as a testbed for studying robotic assembly of NASA hardware with passive compliance. Vector analysis was employed to derive a closed-form solution for the end-effector inverse kinematic transformation. A computationally efficient numerical solution was obtained for the end-effector forward kinematic transformation using Newton-Raphson method. Three trajectory planning schemes, two for fine motion and one for gross motion, were developed for the end-effector. Experiments conducted to evaluate the performance of the trajectory planning schemes showed excellent tracking quality with minimal errors. Current activities focus on implementing the developed trajectory planning schemes on mating and demating space-rated connectors and using the compliant platform to acquire forces/torques applied on the end-effector during the assembly task

Human factors in space telepresence

The problems of interfacing a human with a teleoperation system, for work in space are discussed. Much of the information presented here is the result of experience gained by the M.I.T. Space Systems Laboratory during the past two years of work on the ARAMIS (Automation, Robotics, and Machine Intelligence Systems) project. Many factors impact the design of the man-machine interface for a teleoperator. The effects of each are described in turn. An annotated bibliography gives the key references that were used. No conclusions are presented as a best design, since much depends on the particular application desired, and the relevant technology is swiftly changing

An intelligent, free-flying robot

The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base

Towards a universal end effector : the design and development of production technology's intelligent robot hand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Engineering and Automation at Massey University

Research into robot hands for industrial use began in the early 1980s and there are now many examples of robot hands in existence. The reason for research into robot hands is that standard robot end effectors have to be designed for each application and are therefore costly. A universal end effector is needed that will be able to perform any parts handling operation or use other tools for other industrial operations. Existing robot hand research would therefore benefit from new concepts, designs and control systems. The Department of Production Technology is developing an intelligent robot hand of a novel configuration, with the ultimate aim of producing a universal end effector. The concept of PTIRH (Production Technology's Intelligent Robot Hand) is that it is a multi-fingered manipulator with a configuration of two thumbs and two fingers. Research by the author for this thesis concentrated on five major areas. First, the background research into the state of the art in robot hand research. Second, the initiation, development and analysis of the novel configuration concept of PTIRH. Third, specification, testing and analysis of air muscle actuation, including design, development and testing of a servo pneumatic control valve for the air muscles. Fourth, choice of sensors for the robot hand, including testing and analysis of two custom made air pressure sensors. Fifth, definition, design, construction, development, testing and analysis of the mechanical structure for an early prototype of PTIRH. Development of an intelligent controller for PTIRH was outside the scope of the author's research. The results of the analysis on the air muscles showed that they could be a suitable direct drive actuator for an intelligent robotic hand. The force, pressure and position sensor results indicate that the sensors could form the basis of the feedback loop for an intelligent controller. The configuration of PTIRH enables it to grasp objects with little reliance on friction. This was demonstrated with an early prototype of the robot hand, which had one finger with actuation and three other static digits, by successfully manually arranging the digits into stable grasps of various objects