Haptic teleoperation of mobile manipulator systems using virtual fixtures.

In order to make the task of controlling Mobile-Manipulator Systems (MMS) simpler, a novel command strategy that uses a single joystick is presented to replace the existing paradigm of using multiple joysticks. To improve efficiency and accuracy, virtual fixtures were implemented with the use of a haptic joystick. Instead of modeling the MMS as a single unit with three redundant degrees-of-freedom (DOF), the operator controls either the manipulator or the mobile base, with the command strategy choosing which one to move. The novel command strategy uses three modes of operation to automatically switch control between the manipulator and base. The three modes of operation are called near-target manipulation mode, off-target manipulation mode, and transportation mode. The system enters near-target manipulation mode only when close to a target of interest, and allows the operator to control the manipulator using velocity control. When the operator attempts to move the manipulator out of its workspace limits, the system temporarily enters transportation mode. When the operator moves the manipulator in a direction towards the manipulator???s workspace the system returns to near-target manipulation mode. In off-target manipulation mode, when the operator moves the manipulator to its workspace limits, the system retracts the arm near to the centre of its workspace to enter and remain in transportation mode. While in transportation mode the operator controls the base using velocity control. Two types of virtual fixtures are used, repulsive virtual fixtures and forbidden region virtual fixtures. Repulsive virtual fixtures are present in the form of six virtual walls forming a cube at the manipulator???s workspace limits. When the operator approaches a virtual wall, a repulsive force is felt pushing the operator???s hand away from the workspace limits. The forbidden region virtual fixtures prevent the operator from driving into obstacles by disregarding motion commands that would result in a collision. The command strategy was implemented on the Omnibot MMS and test results show that it was successful in improving simplicity, accuracy, and efficiency when teleoperating a MMS

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

A Review of Pneumatic Actuators Used for the Design of Medical Simulators and Medical Tools

International audienc

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Improving the skills of forest harvester operators

Forestry suffers from a shortage of trained machine operators, which jeopardises efficient and productive operations. Extensive training is required to skilfully master the complex tasks of operators of forest harvesters and forest forwarders. Therefore, the digitisation of the industry envisages training and support systems on machines that provide real-time support to operators, both on-site and remotely. The aim of this thesis was to improve training methods and pave the way for the development of future operator support systems, therefore a detailed analysis of harvester operators' work tasks, focussing on motor control skills and cognitive (work)load, was conducted. The work was guided by the following two general research questions, which were systematically answered throughout the studies presented in this thesis. (1) How can training methods for robotic arm operators be improved by analysing performance limiting factors in the bimanual control of the robotic cranes and (2) How can the machine operators be effectively supported with different sensorimotor support systems to ensure high level performance? To this end, a multi-pronged approach using qualitative and quantitative methods was adopted and five scientific studies were carried out. For three quantitative laboratory studies, a multi-joint robotic manipulator was designed and programmed as a simulation environment, which in its basic layout resembles the crane of real forestry machines. To identify the challenges in learning the motor control of such robotic cranes, this work focussed on the joystick control of the individual joints (joint control) or the movement of the tip (end-effector) of the robotic crane. Two experimental studies on the acquisition of operating skills with the two different control schemes, showed that in spite of a gain in mental workload reduction with end-effector control, movement accuracy remains difficult with both control schemes. This refers with joint control to the challenging use of the joints involved in the fine control of the robotic crane and with end-effector control to a general lack of accuracy. In a third study, visual and auditory (sonification) support systems were implemented in the simulation environment and compared for increasing accuracy. Auditory support systems showed higher effectiveness, which depends on initial operator performance level. In summary, this thesis has shown that behavioural analysis at the level of joystick movements and the analysis of crane movements can be very fruitful for studying the development of human control skills and deriving new performance indicators that can be used in operator training and the design of different operator support systems. The development of machines with increasing technical operator support will potentially lead to new challenges in real-world operation, where the management of cognitive workload and the detrimental effects, specifically of cognitive underload conditions, will require a rethinking and design of the operators’ work

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Limited Information Shared Control and its Applications to Large Vehicle Manipulators

Diese Dissertation beschäftigt sich mit der kooperativen Regelung einer mobilen Arbeitsmaschine, welche aus einem Nutzfahrzeug und einem oder mehreren hydraulischen Manipulatoren besteht. Solche Maschinen werden für Aufgaben in der Straßenunterhaltungsaufgaben eingesetzt. Die Arbeitsumgebung des Manipulators ist unstrukturiert, was die Bestimmung einer Referenztrajektorie erschwert oder unmöglich macht. Deshalb wird in dieser Arbeit ein Ansatz vorgeschlagen, welcher nur das Fahrzeug automatisiert, während der menschliche Bediener ein Teil des Systems bleibt und den Manipulator steuert. Eine solche Teilautomatisierung des Gesamtsystems führt zu einer speziellen Klasse von Mensch-Maschine-Interaktionen, welche in der Literatur noch nicht untersucht wurde: Eine kooperative Regelung zwischen zwei Teilsystemen, bei der die Automatisierung keine Informationen von dem vom Menschen gesteuerten Teilsystem hat. Deswegen wird in dieser Arbeit ein systematischer Ansatz der kooperativen Regelung mit begrenzter Information vorgestellt, der den menschlichen Bediener unterstützen kann, ohne die Referenzen oder die Systemzustände des Manipulators zu messen. Außerdem wird ein systematisches Entwurfskonzept für die kooperative Regelung mit begrenzter Information vorgestellt. Für diese Entwurfsmethode werden zwei neue Unterklassen der sogenannten Potenzialspiele eingeführt, die eine systematische Berechnung der Parameter der entwickelten kooperativen Regelung ohne manuelle Abstimmung ermöglichen. Schließlich wird das entwickelte Konzept der kooperativen Regelung am Beispiel einer großen mobilen Arbeitsmaschine angewandt, um seine Vorteile zu ermitteln und zu bewerten. Nach der Analyse in Simulationen wird die praktische Anwendbarkeit der Methode in drei Experimenten mit menschlichen Probanden an einem Simulator untersucht. Die Ergebnisse zeigen die Überlegenheit des entwickelten kooperativen Regelungskonzepts gegenüber der manuellen Steuerung und der nicht-kooperativen Steuerung hinsichtlich sowohl der objektiven Performanz als auch der subjektiven Bewertung der Probanden. Somit zeigt diese Dissertation, dass die kooperative Regelung mobiler Arbeitsmaschinen mit den entwickelten theoretischen Konzepten sowohl hilfreich als auch praktisch anwendbar ist

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Whole-hand input

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 1992.Includes bibliographical references (p. 219-233).by David Joel Sturman.Ph.D