Semi-autonomous Mode Research Articles

Validating extended neglect tolerance model for human robot interactions in humanoid soccer robots

SUMMARYEstimating robot performance in human robot teams is a vital problem in human robot interaction community. In a previous work, we presented extended neglect tolerance model for estimation of robot performance, where the human operator switches control between robots sequentially based on acceptable performance levels, taking into account any false alarms in human robot interactions. Task complexity is a key parameter that directly impacts the robot performance as well as the false alarms occurrences. In this paper, we validate the extended neglect tolerance model for two robot tasks of varying complexity levels. We also present the impact of task complexity on robot performance estimations and false alarms demands. Experiments were performed with real and virtual humanoid soccer robots across tele-operated and semi-autonomous modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the extended neglect tolerance model predictions for both real and virtual robot experiments. Experiments also showed that the task complexity is directly proportional to false alarm demands and inversely proportional to robot performance.

An Advanced Patient Lift and Transfer Device for the Home

The home lift, position, and rehabilitation (HLPR) chair has a unique design and novel capabilities when compared with conventional powered wheelchairs. In addition to mobility, it provides lift and can transfer patients. Even though medical devices are developing at a rapid pace today, an aspect that is often overlooked in these developments is adherence to “rider safety standards.” The contributions of this paper are threefold: (i) novel design of a lift and transfer system, (ii) experiments and results toward improved stability test designs that include HLPR-type devices to meet rider safety standards, and (iii) autonomous navigation and control based on nonlinear system theory of dynamic feedback linearization. Stability experimental results show promise for multipurpose patient mobility, lift, and transfer devices such as HLPR. A method for autonomous maneuvers was tested in simulation and experiments. We also expect the autonomous or semi-autonomous mobility mode of the vehicle to be useful for riders who have potential neural and cognitive impairments.

False Alarm Metrics for Human–Robot Interactions in Service Robots

Human–robot teams combining the complementary capabilities of robots and humans towards solving potentially complex service tasks are gaining widespread popularity. Many of these tasks will involve close interactions between the robot and the human it serves, thereby making safety a crucial parameter. Erroneous interactions that inevitably arises between the human and the robot cause accidents in service robotic applications. Currently, there are no metrics available in the human–robot interaction community for analyzing erroneous interactions. In this paper, we put forward a new class of false alarm metrics to define, classify and quantify the effects of erroneous interactions in human robot teams, and explore the relationship between false alarms and safety in service robots. We extend the receiver operating characteristics (ROC) curve commonly used in the signal processing community to classify robots based on their associated risks. We also show the utility of the designed false alarm metrics and extended ROC curve by applying them to a service robot, Robo-Erectus@Home, across tele-operation and semi-autonomous modes of autonomy.

A six-legged robot-based system for humanitarian demining missions

Removal of antipersonnel landmines is a worldwide problem currently requiring the use of new technologies such as improved sensors and efficient mobile robots. This paper is focused on the description of a system that relies on a walking robot that carries sensors to detect and locate antipersonnel landmines efficiently and effectively. The paper describes the main features of the walking robot and additional equipment purposefully developed for this kind of application. The overall system consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global positioning system, a remote supervisor computer and the SILO6 walking robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency. Some of the achievements already accomplished with this system are reported.

DYLEMA: Using walking robots for landmine detection and location

Detection and removal of antipersonnel landmines is an important worldwide concern. A huge number of landmines has been deployed over the last twenty years, and demining will take several more decades, even if no more mines were deployed in future. An adequate mine-clearance rate can only be achieved by using new technologies such as improved sensors, efficient manipulators and mobile robots. This paper presents some basic ideas on the configuration of a mobile system for detecting and locating antipersonnel landmines efficiently and effectively. The paper describes the main features of the overall system, which consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global-positioning system, a remote supervisor computer and a legged robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency.

INTEGRATION OF 3D PROXIMITY SCANNER TO ORPHEUS ROBOTIC SYSTEM

Orpheus mobile robot is a teleoperated device primarily designed for remote exploration of hazardous places and rescue missions. The robot is able to operate both indoors and outdoors, is made to be durable and reliable. The device is controlled through advanced user interface with joystick and head mounted display with inertial head movement sensor. To increase the system's operability in difficult conditions (fog, smoke, dark), 3D proximity scanner controlled by on-board processor through Ethernet connection was integrated to it. The scanner also represents a step towards semi-autonomous mode of the robot.

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

Semi-autonomous evolution of object models for adaptive object recognition

The paper presents a semi-autonomous model evolution approach to object recognition under variable perceptual conditions. The approach assumes that (i) the system has to recognize objects on separate images of a sequence, and (ii) the images demonstrate the variability of conditions under which objects are perceived (gradual change in resolution, lighting, positioning). The adaptation of object models is executed due to perceived, over a sequence of images, variabilities of object characteristics. This adaptation involves (i) the application of learned models to the next image, (ii) the monitoring of recognition effectiveness of the models, and (iii) an activation of learning processes if needed (i.e., when the recognition effectiveness of the models decreases). Model adaptation (evolution) integrates recognition processes of computer vision with incremental knowledge acquisition processes of machine learning in a closed loop. The paper presents both an outline of the iterative evolution methodology and the investigation of an incremental model generalization approach using the example of a texture recognition problem. Experiments were run in a semi-autonomous mode where a teacher secured soundness behavior of the evolution system. The experiments are compared for three system configurations: (i) a one-level control structure, (ii) a two-level control structure, and (iii) a two-level control structure with data filtering. The obtained results are evaluated for system recognition effectiveness, recognition stability, and predictability of evolved models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation

NASA scenarios for lunar and planetary missions include robotic vehicles that function in both teleoperated and semi-autonomous modes. Under teleoperation, on-board stereo cameras may provide 3-D scene information to human operators via stereographic displays; likewise, under semi-autonomy, machine stereo vision may provide 3-D information for obstacle avoidance. In the past, the slow speed of machine stereo vision systems has posed a hurdle to the semi-autonomous scenario; however, recent work at JPL and other laboratories has produced stereo systems with high reliability and near real-time performance for low-resolution image pairs. In particular, JPL has taken a significant step by achieving the first autonomous, cross-country robotic traverses (of up to 100 meters) to use stereo vision, with all computing on-board the vehicle. Here, we describe the stereo vision system, including the underlying statistical model and the details of the implementation. The statistical and algorithmic aspects employ random field models of the disparity map, Bayesian formulations of single-scale matching, and area-based image comparisons. The implementation builds bandpass image pyramids and produces disparity maps from the 60×64 level of the pyramids at rates of up to two seconds per image pair. All vision processing is done in one 68020 augmented with Datacube image processing boards. We argue that the overall approach provides a unifying paradigm for practical, domain-independent stereo ranging. We close with a discussion of practical and theoretical issues involved in evaluating and extending the performance of the stereo system.