Coordination and Longevity in Multi-Robot Teams Involving Miniature Robots

Drenner, Andrew; Janssen, Michael; Kottas, Apostolos; Kossett, Alex; Carlson, Casey; Lloyd, Ryan; Papanikolopoulos, Nikolaos

doi:10.1007/s10846-013-9830-x

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…According to the obtained centers and spread width, the training data sample is split into training subsets as (11), where η is a constant for controlling the overlap region of the training subsets and the size of training subsets increases as η increases, as well as computational time.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

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Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen

Liu

et al. 2015

Int J of Soc Robotics

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An intention understanding model based on twolayer fuzzy support vector regression is proposed in humanrobot interaction, where fuzzy c-means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize the transparent communication by understanding customers' order intentions at a bar, in such a way that the social relationship between bar staffs and customers becomes smooth. To demonstrate the aptness of intention understanding model, experiments are designed in term of relationship between emotion-age-gender-nationality and order intention. Results show that the proposal obtains an intention understanding accuracy of 70 %/72 %/80 % with clusters number C = 2/3/6 (according to different genders/ages/nationalities), which is 23 %/26 %/33 % and 35.5 %/37.5 %/45.5 % higher than that of support vector regression (SVR) and back propagation neural networks (BPNN), respectively; the computational time of proposal is about 0.976 s/0.935 s/0.67 s with clusters number C = 2/3/6, while 1.889 s for SVR and 3.505 s for BPNN. Additionally, the preliminary application experiment is performed in the developing human-robot interaction system, called mascot robot system, where the experiment is performed in a scenario of "drinking at a bar", result shows that the bar lady robot obtains an accuracy of 77.8 % for understanding customers' order intentions and receives a satisfaction evaluation of "satisfied". According to the preliminary application, the proposal is being extended to an ordering system in the bar for business communication.

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Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…Many researches conduct unveiling the importance of properly designed adaptive HRI strategies to adjust the robots' behaviors for smooth communication [9][10][11]. Still, HRI is often not so easy because robots lack understanding of the people's internal states, e.g., intentions, rather than just social behaviors, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen

Liu

et al. 2015

Int J of Soc Robotics

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“…A marsupial architecture with a large amount of passenger vehicles per carrier vehicle is studied in [11]. The authors detail the hardware of the carrier and passenger vehicles and present a vision system for the docking of the vehicles.…”

Section: B Literature Reviewmentioning

confidence: 99%

Path planning for information acquisition and evasion using marsupial vehicles

Fargeas

Kabamba

Girard

2015

2015 American Control Conference (ACC)

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This paper considers a path planning problem with two marsupial vehicles (one carrier vehicle and one passenger vehicle that is deployed by the carrier vehicle) exploring a planar area. This work is motivated by multiagent intelligence, surveillance, and reconnaissance missions in contested environments. The vehicles are heterogeneous, e.g., the carrier vehicle is faster than the passenger vehicle or the passenger vehicle possesses better sensors than the carrier vehicle. The vehicles are to gather a finite amount of information about an object of interest using their sensors while minimizing the likelihood of their detection by an opponent. Necessary conditions for optimal solutions are given and straight line travel for both vehicles is shown to be optimal. The results are illustrated in several simulation examples.

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“…Multi-robot system has been attracting more and more attentions with advantages such as parallelism and robustness [1]. Existing researches have focused on the problems of tasks and resource distribution, conflict resolution, cooperative planning, swarm intelligence, etc [2]- [6]. With the expansion of applications, it is noted that some practical tasks involving environmental detection, mine exploration or survival search and rescue often require the robots to entrance many narrow spaces, which means the size of robots shall be reduced with on-board battery and computing ability.…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Docking Approach Based on Embedded Vision

Zhao¹,

Zhiqiang²,

Gu³

et al. 2016

International Journal of Robotics and Automation

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Docking is an essential yet challenging problem to the deployment of marsupial robotic system involving a mother robot and several child robots, as the technologies such as rail-line tracking and global vision measurement are not suitable for applications in unknown environments. This chapter presents a coordinated docking approach based on embedded vision of the child robot collaborated by the mother robot with an innovative lifting docking station. The child robot utilizes a complementary metal oxide semiconductor (CMOS) camera to capture and extract image features related to a benchmark attached on the docking station, and the internal correlation between its heading direction and the entry direction of docking station is then derived, which makes the docking process be concise and convenient. On this basis, a coordinated docking task model is built and the transition conditions among task states are presented. The precision analysis of the docking is also discussed. The experimental results prove the effectiveness of the proposed approach.

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Coordination and Longevity in Multi-Robot Teams Involving Miniature Robots

Cited by 9 publications

References 18 publications

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Path planning for information acquisition and evasion using marsupial vehicles

A Coordinated Docking Approach Based on Embedded Vision

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