Creating an Objective Methodology for Human-Robot Team Configuration Selection

As technology has been advancing and designers have been looking to future applications, it has become increasingly evident that robotic technology can be used to supplement, augment, and improve human performance of tasks. Team members can be combined in various combinations to better utilize their capabilities and skills to create more efficient and diversified operational teams. A primary obstacle to integrating new robotic technology has been the inability to quantitatively compare overall team performance between very different team configurations without limiting the analysis to a few metrics. To-date, mission designers have arbitrarily assigned importance to mission parameters, subjectively limiting the search space. While this has been effective at evaluating individual mission plans, the arbitrary evaluation criteria has made a straightforward comparison between different research projects and ranking scales impossible. The question then becomes how to select an objective set of criteria for any given problem. It is this final question that this research sought to answer. A methodology was developed to facilitate performance comparison amongst heterogeneous human and robot teams. This methodology makes no assumptions about mission priorities or preferences. Instead, it provides an objective, generic, quantitative method to reduce the complexity of the mission designer's decision space. It employs an heuristic, greedy objective reduction algorithm to reduce problem complexity and a multi-objective genetic algorithm to explore the design space. The human-robot team configuration selection problem was utilized as the application that motivated this research. The methodology, however, will be applicable to a wider domain of research. It will provide a structure to enable broader search of the design space, exploration of the differences between performance metrics, and comparison of optimization models that facilitate evaluation of the design options

Common Metrics for Human-Robot Interaction

This paper describes an effort to identify common metrics for task-oriented human-robot interaction (HRI). We begin by discussing the need for a toolkit of HRI metrics. We then describe the framework of our work and identify important biasing factors that must be taken into consideration. Finally, we present suggested common metrics for standardization and a case study. Preparation of a larger, more detailed toolkit is in progress

Applying Control Abstraction to the Design of Human–Agent Teams

Levels of Automation (LOA) provide a method for describing authority granted to automated system elements to make individual decisions. However, these levels are technology-centric and provide little insight into overall system operation. The current research discusses an alternate classification scheme, referred to as the Level of Human Control Abstraction (LHCA). LHCA is an operator-centric framework that classifies a system’s state based on the required operator inputs. The framework consists of five levels, each requiring less granularity of human control: Direct, Augmented, Parametric, Goal-Oriented, and Mission-Capable. An analysis was conducted of several existing systems. This analysis illustrates the presence of each of these levels of control, and many existing systems support system states which facilitate multiple LHCAs. It is suggested that as the granularity of human control is reduced, the level of required human attention and required cognitive resources decreases. Thus, it is suggested that designing systems that permit the user to select among LHCAs during system control may facilitate human-machine teaming and improve the flexibility of the system

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Smart operators: How Industry 4.0 is affecting the worker's performance in manufacturing contexts

Abstract The fourth industrial revolution is affecting the workforce at strategical, tactical, and operational levels and it is leading to the development of new careers with precise and specific skills and competence. The implementation of enabling technologies in the industrial context involves new types of interactions between operators and machines, interactions that transform the industrial workforce and have significant implications for the nature of the work. The incoming generation of Smart Operators 4.0 is characterised by intelligent and qualified operators who perform the work with the support of machines, interact with collaborative robots and advanced systems, use technologies such as wearable devices and augmented and virtual reality. The correct interaction between the workforce and the various enabling technologies of the 4.0 paradigm represents a crucial aspect of the success of the smart factory. However, this interaction is affected by the variability of human behaviour and its reliability, which can strongly influence the quality, safety, and productivity standards. For this reason, this paper aims to provide a clear and complete analysis of the different types of smart operators and the impact of 4.0 enabling technologies on the performance of operators, evaluating the stakeholders involved, the type of interaction, the changes required for operators in terms of added and removed work, and the new performance achieved by workers

A Survey of Multi-Agent Human-Robot Interaction Systems

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.Comment: 23 pages, 7 figure