A distributed telerobotics construction set

During the course of our research on distributed telerobotic systems, we have assembled a collection of generic, reusable software modules and an infrastructure for connecting them to form a variety of telerobotic configurations. This paper describes the structure of this 'Telerobotics Construction Set' and lists some of the components which comprise it

Trust-Based Control of (Semi)Autonomous Mobile Robotic Systems

Despite great achievements made in (semi)autonomous robotic systems, human participa-tion is still an essential part, especially for decision-making about the autonomy allocation of robots in complex and uncertain environments. However, human decisions may not be optimal due to limited cognitive capacities and subjective human factors. In human-robot interaction (HRI), trust is a major factor that determines humans use of autonomy. Over/under trust may lead to dispro-portionate autonomy allocation, resulting in decreased task performance and/or increased human workload. In this work, we develop automated decision-making aids utilizing computational trust models to help human operators achieve a more effective and unbiased allocation. Our proposed decision aids resemble the way that humans make an autonomy allocation decision, however, are unbiased and aim to reduce human workload, improve the overall performance, and result in higher acceptance by a human. We consider two types of autonomy control schemes for (semi)autonomous mobile robotic systems. The first type is a two-level control scheme which includes switches between either manual or autonomous control modes. For this type, we propose automated decision aids via a computational trust and self-confidence model. We provide analytical tools to investigate the steady-state effects of the proposed autonomy allocation scheme on robot performance and human workload. We also develop an autonomous decision pattern correction algorithm using a nonlinear model predictive control to help the human gradually adapt to a better allocation pattern. The second type is a mixed-initiative bilateral teleoperation control scheme which requires mixing of autonomous and manual control. For this type, we utilize computational two-way trust models. Here, mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback cue sent by the robot is dynamically scaled with a function of computational robot-to-human trust to reduce humans physical workload. Using the proposed control schemes, our human-in-the-loop tests show that the trust-based automated decision aids generally improve the overall robot performance and reduce the operator workload compared to a manual allocation scheme. The proposed decision aids are also generally preferred and trusted by the participants. Finally, the trust-based control schemes are extended to the single-operator-multi-robot applications. A theoretical control framework is developed for these applications and the stability and convergence issues under the switching scheme between different robots are addressed via passivity based measures

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 370)

This bibliography lists 219 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Dec. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

SPATIAL PERCEPTION AND ROBOT OPERATION: THE RELATIONSHIP BETWEEN VISUAL SPATIAL ABILITY AND PERFORMANCE UNDER DIRECT LINE OF SIGHT AND TELEOPERATION

This dissertation investigated the relationship between the spatial perception abilities of operators and robot operation under direct-line-of-sight and teleoperation viewing conditions. This study was an effort to determine if spatial ability testing may be a useful tool in the selection of human-robot interaction (HRI) operators. Participants completed eight cognitive ability measures and operated one of four types of robots under tasks of low and high difficulty. Performance for each participant was tested during both direct-line-of-sight and teleoperation. These results provide additional evidence that spatial perception abilities are reliable predictors of direct-line-of-sight and teleoperation performance. Participants in this study with higher spatial abilities performed faster, with fewer errors, and less variability. In addition, participants with higher spatial abilities were more successful in the accumulation of points. Applications of these findings are discussed in terms of teleoperator selection tools and HRI training and design recommendations with a human-centered design approach

Telerobotic Sensor-based Tool Control Derived From Behavior-based Robotics Concepts

@font-face { font-family: TimesNewRoman ; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0in 0in 0.0001pt; font-size: 12pt; font-family: Times New Roman ; }div.Section1 { page: Section1; } Teleoperated task execution for hazardous environments is slow and requires highly skilled operators. Attempts to implement telerobotic assists to improve efficiency have been demonstrated in constrained laboratory environments but are not being used in the field because they are not appropriate for use on actual remote systems operating in complex unstructured environments using typical operators. This work describes a methodology for combining select concepts from behavior-based systems with telerobotic tool control in a way that is compatible with existing manipulator architectures used by remote systems typical to operations in hazardous environment. The purpose of the approach is to minimize the task instance modeling in favor of a priori task type models while using sensor information to register the task type model to the task instance. The concept was demonstrated for two tools useful to decontamination & dismantlement type operations—a reciprocating saw and a powered socket tool. The experimental results demonstrated that the approach works to facilitate traded control telerobotic tooling execution by enabling difficult tasks and by limiting tool damage. The role of the tools and tasks as drivers to the telerobotic implementation was better understood in the need for thorough task decomposition and the discovery and examination of the tool process signature. The contributions of this work include: (1) the exploration and evaluation of select features of behavior-based robotics to create a new methodology for integrating telerobotic tool control with positional teleoperation in the execution of complex tool-centric remote tasks, (2) the simplification of task decomposition and the implementation of sensor-based tool control in such a way that eliminates the need for the creation of a task instance model for telerobotic task execution, and (3) the discovery, demonstrated use, and documentation of characteristic tool process signatures that have general value in the investigation of other tool control, tool maintenance, and tool development strategies above and beyond the benefit sustained for the methodology described in this work

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

Probabilistic Human-Robot Information Fusion

This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability

Closed-loop real-time control on distributed networks

This thesis is an effort to develop closed-loop control strategies on computer networks and study their stability in the presence of network delays and packet losses. An algorithm using predictors was designed to ensure the system stability in presence of network delays and packet losses. A single actuator magnetic ball levitation system was used as a test bed to validate the proposed algorithm. A brief study of real-time requirements of the networked control system is presented and a client-server architecture is developed using real-time operating environment to implement the proposed algorithm. Real-time performance of the communication on Ethernet based on user datagram protocol (UDP) was explored and UDP is presented as a suitable protocol for networked control systems. Predictors were designed based on parametric estimation models. Autoregressive (AR) and autoregressive moving average (ARMA) models of various orders were designed using MATLAB and an eighth order AR model was adopted based on the best-fit criterion. The system output was predicted several steps ahead using these predictors and control output was calculated using the predictions. This control output output was used in the events of excessive network delays to maintain system stability. Experiments employing simulations of consecutive packet losses and network delays were performed to validate the satisfactory performance of the predictor based algorithm. The current system compensates for up to 20 percent data losses in the network without loosing stability

Graphical interface between the CIRSSE testbed and CimStation software with MCS/CTOS

This research is concerned with developing a graphical simulation of the testbed at the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) and the interface which allows for communication between the two. Such an interface is useful in telerobotic operations, and as a functional interaction tool for testbed users. Creating a simulated model of a real world system, generates inevitable calibration discrepancies between them. This thesis gives a brief overview of the work done to date in the area of workcell representation and communication, describes the development of the CIRSSE interface, and gives a direction for future work in the area of system calibration. The CimStation software used for development of this interface, is a highly versatile robotic workcell simulation package which has been programmed for this application with a scale graphical model of the testbed, and supporting interface menu code. A need for this tool has been identified for the reasons of path previewing, as a window on teleoperation and for calibration of simulated vs. real world models. The interface allows information (i.e., joint angles) generated by CimStation to be sent as motion goal positions to the testbed robots. An option of the interface has been established such that joint angle information generated by supporting testbed algorithms (i.e., TG, collision avoidance) can be piped through CimStation as a visual preview of the path