Multiple targets enclosure by robotic swarm

Kubo, Michinori; Satō, Hiroshi; Yoshimura, Tatsuro; Yamaguchi, Akihiro; Tanaka, Takahiro

doi:10.1016/j.robot.2014.03.014

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…That is, if the relationship between a robot and its reference robot is considered as a link in graph theory, the graph of the group of robots must be a Hamiltonian cycle. The authors proved the convergence to the goal state of the target enclosing task under this constraint 3 . Fig.3 The most powerful signal (MOPS) direction…”

Section: Takayama's Target Enclosing Modelmentioning

confidence: 93%

A method of enclosing a target by robot swarm with limited range homogeneous signal emitter

Kubo¹,

Satō²,

Yamaguchi³

et al. 2015

JRNAL

Self Cite

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This paper presents a robot swarm model to enclose a target. The robots use information of the target and each of their neighbors. Every robot has its own, standard torch--like signal emitter and they can observe the sum of the intensity of their torches. In this paper, the robots identify their nearest neighbor by choosing the emitting source with the strongest intensity. We expect that this new approach makes the robot swarm more simple and scalable. We confirm this model by computer simulations.

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Section: Takayama's Target Enclosing Modelmentioning

confidence: 93%

A method of enclosing a target by robot swarm with limited range homogeneous signal emitter

Kubo¹,

Satō²,

Yamaguchi³

et al. 2015

JRNAL

Self Cite

View full text Add to dashboard Cite

show abstract

“…with r the distance to point of interest, b it the bearing toward this point, b ip the bearing toward robot i predecessor in the circle (i.e. the closest robot in front), and f , k, parameters of the pursuit behavior similar to those used by Kubo et al [49]. Cyclic pursuit is the control algorithm selected by the focus group to represent happiness in our second user study (see Section 5).…”

Section: Cyclic Pursuitmentioning

confidence: 99%

Collective expression: how robotic swarms convey information with group motion

St-Onge

Levillain

Zibetti

et al. 2019

Paladyn, Journal of Behavioral Robotics

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When faced with the need of implementing a decentralized behavior for a group of collaborating robots, strategies inspired from swarm intelligence often avoid considering the human operator, granting the swarm with full autonomy. However, field missions require at least to share the output of the swarm to the operator. Unfortunately, little is known about the users’ perception of group behavior and dynamics, and there is no clear optimal interaction modality for swarms. In this paper, we focus on the movement of the swarm to convey information to a user: we believe that the interpretation of artificial states based on groups motion can lead to promising natural interaction modalities. We implement a grammar of decentralized control algorithms to explore their expressivity. We define the expressivity of a movement as a metric to measure how natural, readable, or easily understandable it may appear. We then correlate expressivity with the control parameters for the distributed behavior of the swarm. A first user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system. We follow up with a small group of users tasked with the design of expressive motion sequences to convey internal states using our grammar of algorithms. We comment on their design choices and we assess the interpretation performance by a larger group of users. We show that some of the internal states were perceived as designed and discuss the parameters influencing the performance.

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“…Our implementation takes a leader in the group to gather the bids of each robot for assignments, based only on their distance to the graph nodes, and allocate the position to each robot. (3) Pursuit as a behavior to patrol around a point of interest, with each robot linear and rotational velocity, similarly to the work of Kubo et al (2014), given by, respectively:…”

Section: Software Ecosystemmentioning

confidence: 99%

Engaging with Robotic Swarms

St-Onge

Côté-Allard

Glette

et al. 2019

J. Hum.-Robot Interact.

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In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

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Multiple targets enclosure by robotic swarm

Cited by 15 publications

References 9 publications

A method of enclosing a target by robot swarm with limited range homogeneous signal emitter

A method of enclosing a target by robot swarm with limited range homogeneous signal emitter

Collective expression: how robotic swarms convey information with group motion

Engaging with Robotic Swarms

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