Auditory Decision Aiding in Supervisory Control of Multiple Unmanned Aerial Vehicles

Donmez, Birsen; Cummings, Mary L.; Graham, Hudson D.

doi:10.1177/0018720809347106

Cited by 29 publications

(27 citation statements)

References 17 publications

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“…Auditory presentation of information can be combined with ongoing visual tasks (Helleberg et al 2003), and these improvements can be particularly important when dealing with multiple UVs, provided that they do not interfere with other auditory warnings (Donmez et al 2009). However, combining the control of a UV with other tasks can impair performance on target detection Wickens 2003, Chen 2008) and reduce situation awareness (Luck et al 2006).…”

Section: Multimodal Interaction With Uvsmentioning

confidence: 99%

Multimodal control of sensors on multiple simulated unmanned vehicles

et al. 2011

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The use of multimodal (speech plus manual) control of the sensors on combinations of one, two, three or five simulated unmanned vehicles (UVs) is explored. Novice controllers of simulated UVs complete a series of target checking tasks. Two experiments compare speech and gamepad control for one, two, three or five UVs in a simulated environment. Increasing the number of UVs has an impact on subjective rating of workload (measured by NASA-Task Load Index), particularly when moving from one to three UVs. Objective measures of performance showed that the participants tended to issue fewer commands as the number of vehicles increased (when using the gamepad control), but, while performance with a single UV was superior to that of multiple UVs, there was little difference across two, three or five UVs. Participants with low spatial ability (measured by the Object Perspectives Test) showed an increase in time to respond to warnings when controlling five UVs. Combining speech with gamepad control of sensors on UVs leads to superior performance on a secondary (respond-to-warnings) task (implying a reduction in demand) and use of fewer commands on primary (move-sensors and classify-target) tasks (implying more efficient operation). STATEMENT OF RELEVANCE: Benefits of multimodal control for unmanned vehicles are demonstrated. When controlling sensors on multiple UVs, participants with low spatial orientation scores have problems. It is proposed that the findings of these studies have implications for selection of UV operators and suggests that future UV workstations could benefit from multimodal control.

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Section: Multimodal Interaction With Uvsmentioning

confidence: 99%

Multimodal control of sensors on multiple simulated unmanned vehicles

et al. 2011

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“…Analoguously to the supervisory role, the feedback of continuously changing states should be provided to the human in dynamical form. This conclusion has been made through experimental validation for the control of multiple UAVs in [28] and [29]. In [1] the authors investigate haptic human-robot team interaction with variable formation.…”

Section: Feedback Interfacesmentioning

confidence: 99%

Control sharing in human-robot team interaction

Musić

Hirche

2017

Annual Reviews in Control

117

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The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in humanrobot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and humanmachine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.

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“…For instance, when the user is provided with too much information, multiple sensory channels could be used to reduce the workload and increase the capability of the user in processing the information [42]. In the suggested framework, we, however, consider having a uni-modal display and assume that all information in that display is of equal weight.…”

Section: Assumptionmentioning

confidence: 99%

Delay‐incorporating observability and predictability analysis of safety‐critical continuous‐time systems

Eskandari

Dumont

Wang

2015

IET Control Theory & Applications

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The authors suggest a framework for human-automation interaction in safety-critical continuous systems under shared control and consider continuous-time linear time-invariant (LTI) dynamics to formalise our physical models mathematically. Their goal is to determine whether or not a given user-interface provides the information required for a certain task, under the assumption that the user does not have access to any information beyond what is provided in the display. They identify observability-based conditions under which a user-interface provides the user with necessary information to accomplish a given task, formulated as a subset of the state space. They, therefore formulate the novel delay-incorporating user-observable subspace, and the delay-incorporating user-predictable subspace and compare them with the space spanned by the combination of the states which create the task. They assume the user is a special type of observer, with capabilities corresponding to different levels of knowledge regarding the current user's input and its derivatives. In addition, they consider that state reconstruction and prediction incorporate a processing delay. IntroductionAs interfaces and their underlying systems become more complex, information beyond what is contained in the interface may not be accessible. We aim to identify tools that assess the correctness of a user-interface for a given task, an especially relevant problem in systems for which intuition and simulation may not be enough to assure that an interface is effective. While in many systems, such as a human-driven car, the user has access to information beyond what is simply contained in the interface, we focus here solely on information contained in the interface, for example, a remotely controlled car or a pilot performing a task in high altitude.Human-factors researchers have frequently pointed out the importance of incorporating human-factors into traditional control theory, which can result in better human-automation interaction [1, 2]. Many researchers have suggested frameworks for humanautomation interaction in order to come up with more effective interaction between the elements of the system [1-3]. Attaining situation awareness (SA) is a human-factor concept which is known to be necessary for the user to have correct interaction with the automation [4,5]. Attaining SA includes three levels of processing the information, (i) perception of the information, (ii) comprehension of the information and (iii) projection or prediction of the information [4,6]. Lack of SA can lead to a faulty control action by the operator because of bad decisions. As we have already assumed that the display is the only source of information for the user, it has to be designed to allow the user perceive, comprehend and predict the desired information to attain correct SA. Hence, the display design procedure requires a careful selection and clear presentation of the information for the user to perform the desired tasks correctly.Some examples which are available in the literatu...

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Auditory Decision Aiding in Supervisory Control of Multiple Unmanned Aerial Vehicles

Cited by 29 publications

References 17 publications

Multimodal control of sensors on multiple simulated unmanned vehicles

Multimodal control of sensors on multiple simulated unmanned vehicles

Control sharing in human-robot team interaction

Delay‐incorporating observability and predictability analysis of safety‐critical continuous‐time systems

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