Search results

In complex environments, the use of technology to enhance the capability of people is commonplace. In rapidly changing and often unpredictable environments, it is not enough that these human-automated “teams” perform well when events go as expected. Instead, the human operators and automated aids must be flexible, capable of responding to rare or unanticipated events. The purpose of this chapter is to discuss the Framework of Automation Use (Dzindolet, Beck, Pierce, & Dawe, 2001) as it relates to adaptive automation. Specifically, our objectives are to: (1) examine a number of factors that determine how people can effectively integrate their activities with their machine partners in fluid environments and (2) consider the implications of these findings for future research.

The purpose of this paper is to investigate the effect on time to complete a task depending on how a human operator interacts with a mobile‐robot. Interaction is investigated using two tele‐operated mobile‐robot systems, three different ways of interacting with robots and several different environments. The speed of a tele‐operator in completing progressively more complicated driving tasks is investigated also.

Tele‐operators are timed completing a series of tasks using a joystick to control a mobile‐robot. They either watch the robot while operating it, or sit at a computer and view scenes remotely on a screen. Cameras are either mounted on the robot, or so that they view both the environment and robot. Tele‐operators complete tests both with and without sensors. One robot system uses an umbilical cable and one uses a radio link.

In simple environments, a tele‐operator may perform better without a sensor system to assist them but in more complicated environments then a tele‐operator may perform better with a sensor system to assist. Tele‐operators may also tend to perform better with a radio link than with an umbilical connection. Tele‐operators sometimes perform better with a camera mounted on the robot compared with pre‐mounted cameras observing the environment (but that depends on tasks being performed).

Tele‐operated systems rely heavily on visual feedback and experienced operators. This paper investigates how to make tasks easier.

The paper suggests that the amount of sensor support should be varied depending on circumstances.

Results show that human tele‐operators perform better without the assistance of a sensor systems in simple environments.

The purpose of this paper is to investigate the effect on completion of mobile‐robot tasks depending on how a human tele‐operator interacts with a sensor system and a mobile‐robot.

Interaction is investigated using two mobile‐robot systems, three different ways of interacting with the robots and several different environments of increasing complexity. In each case, the operation is investigated with and without sensor systems to assist an operator to move a robot through narrower and narrower gaps and in completing progressively more complicated driving tasks. Tele‐operators used a joystick and either watched the robot while operating it, or sat at a computer and viewed scenes remotely on a screen. Cameras are either mounted on the robot to view the space ahead of the robot or mounted remotely so that they viewed both the environment and robot. Every test is compared with sensor systems engaged and with them disconnected.

A main conclusion is that human tele‐operators perform better without the assistance of sensor systems in simple environments and in those cases it may be better to switch‐off the sensor systems or reduce their effect. In addition, tele‐operators sometimes performed better with a camera mounted on the robot compared with pre‐mounted cameras observing the environment (but that depended on tasks being performed).

Tele‐operators completed tests both with and without sensors. One robot system used an umbilical cable and one used a radio link.

The paper quantifies the difference between tele‐operation control and sensor‐assisted control when a robot passes through narrow passages. This could be an useful information when system designers decide if a system should be tele‐operated, automatic or sensor‐assisted. The paper suggests that in simple environments then the amount of sensor support should be small but in more complicated environments then more sensor support needs to be provided.

The paper investigates the effect of completing mobile‐robot tasks depending on whether a human tele‐operator uses a sensor system or not and how they interact with the sensor system and the mobile‐robot. The paper presents the results from investigations using two mobile‐robot systems, three different ways of interacting with the robots and several different environments of increasing complexity. The change in the ability of a human operator to complete progressively more complicated driving tasks with and without a sensor system is presented and the human tele‐operators performed better without the assistance of sensor systems in simple environments.

The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index.

Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction.

By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms.

The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

The main objective of this study is to identify and define three sets of factors that might be useful for designing a disaster monitoring and response system.

First, a literature (meta) analysis is presented using academic research. The method was mainly based on a review of the scientific literature. The paper then identifies three sets of factors that may be employed when designing disaster monitoring and response systems.

The paper finds that successful operation of an organization that hosts a disaster monitoring system requires that operators and computers work together.

The model itself in this study is not all‐inclusive. An issue that deserves to be looked into is what role other technical, human, and organizational factors play in system performance.

The importance of disaster monitoring and response systems increased in recent years because of an increase in the numbers of deaths, the numbers of people affected by disasters and their devastating impacts on human life, economy and environment. These systems have the potential to significantly reduce losses from natural disasters.

This study proposes a model which may be valuable to state and federal agencies, public sector managers and administrators, system analysts, trainers in disaster management, researchers and practitioners involved in disaster and emergency response studies, managers of police, fire, and ambulance systems, and mayors and governors.

The purpose of this paper is to describe the use of simple expert systems to improve the performance of tele‐operated mobile robots and ultrasonic sensor systems. The expert systems interpret data from the joystick and sensors and identify potentially hazardous situations and then recommend safe courses of action so that tele‐operated mobile‐robot tasks can be completed more quickly.

The speed of a tele‐operator in completing progressively more complicated driving tasks is investigated while using a simple expert system. Tele‐operators were timed completing a series of tasks using a joystick to control a mobile robot through a simple expert system that assisted them with driving the robot while using ultrasonic sensors to avoid obstacles. They either watched the robot while operating it or sat at a computer and viewed scenes remotely on a screen from a camera mounted on the robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The system used an umbilical cable to connect to the robot.

The simple expert systems consistently performed faster than the other systems. Results are compared with the most recently published results and show a significant improvement. In addition, in simple environments, tele‐operators performed better without a sensor system to assist them but in more complicated environments than tele‐operators performed better with the sensor systems to assist.

Simple expert systems are shown to improve the operation of a tele‐operated mobile robot with an obstacle avoidance systems fitted.

Tele‐operated systems rely heavily on visual feedback and experienced operators. This paper investigates how to make tasks easier. Simple expert systems are shown to improve the operation of a tele‐operated mobile robot. The paper also suggests that the amount of sensor support should be varied depending on circumstances.

The simple expert systems are shown in this paper to improve the operation of a tele‐operated mobile robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The results are compared with a tele‐operator driving a mobile robot without any assistance from the expert systems or sensors and they show a significant improvement.

This study aims to realize natural and effort-saving motion behavior and improve effectiveness for different operators in human–robot force cooperation.

The parameter of admittance model is identified by deep deterministic policy gradient (DDPG) to realize human–robot force cooperation for different operators in this paper. The movement coupling problem of hybrid robot is solved by realizing position and pose drags. In DDPG, minimum jerk trajectory is selected as the reward objective function, and the variable prioritized experience replay is applied to balance the exploration and exploitation.

A series of simulations are implemented to validate the superiority and stability of DDPG. Furthermore, three sets of experiments involving mass parameter, damping parameter and DDPG are implemented, the effect of DDPG in real environment is validated and could meet the cooperation demand for different operators.

DDPG is applied in admittance model identification to realize human–robot force cooperation for different operators. And minimum jerk trajectory is introduced into reward objective to meet requirement of human arm free movements. The algorithm proposed in this paper could be further extended in the other operation task.

Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).

Social factors are an under‐researched but important aspect in the success of manufacturing cells. This paper sets out to investigate the impact and importance of various human factors within a socio‐technical system such as team‐based cellular manufacturing (TBCM).

A questionnaire survey was designed to provide information about human factors in TBCM. The survey was conducted at four medium‐to‐large size organisations in Australia and Switzerland where participants were required to be working within a TBCM environment and included managers, team leaders, and operators. A set of research questions and hypotheses was developed and tested.

It was found that human issues account for a significant proportion of problems within team‐based manufacturing cells. Of the eight human factors tested in this survey, communication, teamwork and training were ranked the most important, while reward/compensation was ranked the least important. Testing showed significant relationships between factors such as companies, positions, experience and team size; therefore most hypotheses were supported.

Traditionally, the research focus has been on the technical aspect of socio‐technical systems such as TBCM. This study offers practitioners and academics a better understanding of the human issues associated with this important form of manufacturing, therefore improving the likelihood of its success.

This paper demonstrates the need for research into the social side of TBCM, while providing an understanding of the important human factors associated with this system.

This chapter provides an overview of the Department of Defense (DoD) laboratory structure to help equipment designers, modelers, and manufacturers determine where research, testing programs, or relevant findings can be found. The chapter includes a discussion of the performance measures and metrics typically used in DoD laboratories and concludes by considering the current state-of-the-art as well as the state-of-the-possible for human performance measurement.