KAIRO 3: A modular reconfigurable robot for search and rescue field missions

Pfotzer, L.; Ruehl, Steffen W.; Heppner, G.; Roennau, Arne; Dillmann, Ruediger

doi:10.1109/robio.2014.7090331

Cited by 24 publications

(24 citation statements)

References 15 publications

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“…38 During development stage, most systems rely on a tethered power source for experimentation. This is confirmed in CONRO 9 and Kairo-3 33 systems. 3.…”

Section: Poweringsupporting

confidence: 65%

“…In this system, it is possible to change position of modules, usually through manual-reconfiguration, but the configuration remains the same. Among snake-like robots, we find ACM I to V 3,55,56,57 Wormbot, 58 KAIRO-3, 33 ReBiS, 36 Modular Amphibious Snake-like Robot, 59 and Lola-OP. 60 KAIRO-3 33 is the latest version of the KAIRO series, which are biologically inspired by inchworm and snake organisms.…”

Section: Manually Configurablementioning

confidence: 96%

“…Lithium-polymer (LiPo) and lithium-ion (Li-ion) batteries are used in Crystalline, 6 M-TRAN, 8 CONRO, 9 Atron, 12 YaMoR, 13 Superbot, 14 Chobie, 16 Roombots, 18 Symbricator, 26 M 3 Express, 30 M-Blocks, 32 Kairo-3, 33 ModRED, 37 and Fable II. 40 In Y1 module, 17 alkaline batteries are used as power source.…”

Section: Poweringmentioning

confidence: 99%

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Current trends in reconfigurable modular robots design

Brunete

Ranganath

Segovia

et al. 2017

International Journal of Advanced Robotic Systems

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This article presents a review on trends in modular reconfigurable robots, comparing the evolution of the features of the most significant robots over the years and focusing on the latest designs. These features are reconfiguration, docking, degrees of freedom, locomotion, control, communications, size, and powering. For each feature, some of the most relevant designs are presented and the current trends in the design are discussed.

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“…38 During development stage, most systems rely on a tethered power source for experimentation. This is confirmed in CONRO 9 and Kairo-3 33 systems. 3.…”

Section: Poweringsupporting

confidence: 65%

Section: Manually Configurablementioning

confidence: 96%

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Current trends in reconfigurable modular robots design

Brunete

Ranganath

Segovia

et al. 2017

International Journal of Advanced Robotic Systems

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“…by solving the follow distributed optimization function min ∑ n i=1 f i (β) s.t. β ∈ R q , as was shown previously each f i :: R q → R are convex function as a result the target function is given by the average of the gradient functions of each node as f * = 1 n ∑ n j=1 ( f i ), taking this into account, it can be evidenced that Equation (6) has the same form of the followingβ…”

Section: Distributed Estimation Consensusmentioning

confidence: 74%

“…Additionally, it is worth highlighting the amount of information that is required to be processed by a single robot in order to satisfying all tasks. Consequently, some researchers focus on the design of semi-autonomous robots as presented in [5,6]. On the other hand, multi-robot research has shown a different alternative to solve the aforementioned task, for instance, the advantages of using robotic swarms in exploration tasks is mentioned in [7,8].…”

Section: Introductionmentioning

confidence: 99%

Robot Swarm Navigation and Victim Detection Using Rendezvous Consensus in Search and Rescue Operations

Cardona

Calderón

2019

Applied Sciences

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Cooperative behaviors in multi-robot systems emerge as an excellent alternative for collaboration in search and rescue tasks to accelerate the finding survivors process and avoid risking additional lives. Although there are still several challenges to be solved, such as communication between agents, power autonomy, navigation strategies, and detection and classification of survivors, among others. The research work presented by this paper focuses on the navigation of the robot swarm and the consensus of the agents applied to the victims detection. The navigation strategy is based on the application of particle swarm theory, where the robots are the agents of the swarm. The attraction and repulsion forces that are typical in swarm particle systems are used by the multi-robot system to avoid obstacles, keep group compact and navigate to a target location. The victims are detected by each agent separately, however, once the agents agree on the existence of a possible victim, these agents separate from the general swarm by creating a sub-swarm. The sub-swarm agents use a modified rendezvous consensus algorithm to perform a formation control around the possible victims and then carry out a consensus of the information acquired by the sensors with the aim to determine the victim existence. Several experiments were conducted to test navigation, obstacle avoidance, and search for victims. Additionally, different situations were simulated with the consensus algorithm. The results show how swarm theory allows the multi-robot system navigates avoiding obstacles, finding possible victims, and settling down their possible use in search and rescue operations.

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