Towards situational awareness from robotic group motion

Levillain, Florent; St-Onge, David; Beltrame, Giovanni; Zibetti, Elisabetta

doi:10.1109/ro-man46459.2019.8956381

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…For example, a projection in front of the robot can catch the attention of a bystander while simultaneously informing about the direction of driving [79]. At the same time, mobile robots need to communicate their state and planning of actions ahead of time, either the inner state (e.g., what is the current mission status [74]) or the perceived world state (e.g., which objects are detected [31]). Humanoids and robotic arms, on the other hand, are often deployed in collaborative scenarios, teaming up with humans.…”

Section: Relationship To Intentmentioning

confidence: 99%

How to Communicate Robot Motion Intent: A Scoping Review

Pascher

Gruenefeld

Schneegaß

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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a) Motion [120] (b) Attention [67] (c) State [117] (d) Instruction [128] Fig. 1. (a) Robot motion intent: The robot communicates its intended motion (e.g., a trajectory of the robot's intended movement path is visualized in Augmented Reality [120]). Furthermore, our analysis revealed three additional types of intent that complement robot motion intents. (b) Attention: A robot aims to catch the user's attention for subsequent movement activity (e.g., by moving its whole body [67]). (c) State: A robot communicates its state so that a human can predict future motions and identify potential conflicts before they occur (e.g., the robot communicates its movement activity with the help of a colored LED stripe [117]). (d) Instruction: The robot aims to provide specific instructions so that the human can assist further movement (e.g., by requesting to open a door [128]).Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge.Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.

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Section: Relationship To Intentmentioning

confidence: 99%

How to Communicate Robot Motion Intent: A Scoping Review

Pascher

Gruenefeld

Schneegaß

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…Nine GIFs were created exploring six different kinds of swarm communicative messages; initiating communication, closing communication, no problem, intervention required, low battery, broken communication 1 . These messages were chosen based on previous work exploring communicative intent in robot swarms [10] and were designed in collaboration with domain experts (graphic design, robotics, and psychology students) and thus followed a "top-down" approach to generating robot behaviours: design the motion before the swarm behavior. Whereas previous works aimed at assessing the perception of common swarm behaviors (e.g., flocking, rendez-vous; [18], [19]), we designed pure motions that will later be implemented as swarm behaviors.…”

Section: B Materialsmentioning

confidence: 99%

“…For instance, when there are multiple objects to keep track of, even if they are few in number, difficulties in detecting changes in the status of the swarm may arise (e.g., [7], [8]). As operators may not always be co-located with the swarm, how the operator visualizes and interprets the movements of the swarm during remote interactions also needs to be considered [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Individual Gestalt factors, such as similarity, proximity, symmetry, and synchrony are also not specific to static visual information, but apply also to moving objects [17]. Group cohesiveness can then further be conceptualised as a multifaceted construct, made up of several different parameters which both individually and collectively contribute to the perception of the swarm [10].…”

Section: Introductionmentioning

confidence: 99%

“…We can potentially leverage group cohesiveness to inform the design of swarm behaviours. Although previous works have established that robotic swarms are able to convey different kinds of messages through their behaviour [10], [4], [18], these have so far followed a mostly bottom-up approach, manipulating mechanical features of the swarm such as velocity, trajectory, and inter-robot distances. How these behaviours are translated on a psychological and cognitive level, and how this can subsequently influence the interpretation of the swarm's message/s has yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bots of a Feather: Exploring User Perceptions of Group Cohesiveness for Application in Robotic Swarms

Stower¹,

Zibetti

St-Onge

2022

2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.

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