A formation behavior for large-scale micro-robot force deployment

Dudenhoeffer, Donald D.; Jones, Michael P.

doi:10.1109/wsc.2000.899900

Cited by 41 publications

(24 citation statements)

References 8 publications

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“…Agent-based modeling methodology has several benefits over other modeling techniques; it captures emergent patterns of system behavior, provides a natural description of a system composed of behavioral entities and is flexible for tuning the complexity of the entities (Bonabeau, 2002). The methodology is used in a wide range of application domains including financial markets [Ergin et al], homeland security applications (Weiss, 2008) and autonomous robots (Dudenhoeffer & Jones, 2000).…”

Section: Methodsmentioning

confidence: 99%

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

Dağli¹,

Ergin²,

Enke³

et al. 2012

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. NO WARRANTY THIS STEVENS INSTITUTE OF TECHNOLOGY AND SYSTEMS ENGINEERING RESEARCH CENTER MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. STEVENS INSTITUTE OF TECHNOLOGY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. STEVENS INSTITUTE OF TECHNOLOGY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT.This material has been approved for public release and unlimited distribution except as restricted below.Internal use:* Permission to reproduce this material and to prepare derivative works from this material for internal use is granted, provided the copyright and "No Warranty" statements are included with all reproductions and derivative works. EXECUTIVE SUMMARYA major challenge to the successful planning and evolution of an acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of an SoS are fully functioning, stand-alone Systems in their own right, they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the SoS. Individual systems typically must be requested to join the SoS construct, and persuaded to interface and cooperate with other Systems to create the "new" capability of the proposed SoS. Current SoS evolution strategies lack a means for modeling the impact of decisions concerning participation or non-participation of any given set of systems on the overall capability of the SoS construct. Without this capability, it is difficult to optimize the SoS design.The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD Systems of Systems (SoS) development efforts do not typically follow the normal program acquisition process described in Do...

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Section: Methodsmentioning

confidence: 99%

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

Dağli¹,

Ergin²,

Enke³

et al. 2012

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“…Agent-based modeling methodology has several benefits over other modeling techniques; it enables Acknowledged SoS manger to capture emergent patterns of system behavior, provides a natural description of a system composed of behavioral entities and is flexible for tuning the complexity of the entities [4]. The methodology is used in a wide range of application domains including financial markets [5], homeland security applications [6] and autonomous robots [7].…”

Section: Research Objectivesmentioning

confidence: 99%

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model: Phase 2

Dağli¹

2013

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE NOV 20132. REPORT TYPE SUPPLEMENTARY NOTES ABSTRACTThe goal of this research is to model the evolution of the architecture of an acknowledged Systems of Systems (SoS) that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD SoS development efforts do not typically follow the program of record acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the non-utopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes. This material has been approved for public release and unlimited distribution except as restricted below.Internal use:* Permission to reproduce this material and to prepare derivative works from this material for internal use is granted, provided the copyright and "No Warranty" statements are included with all reproductions and derivative works.External use:* This material may be reproduced in its entirety, without modification, and freely distributed in written or electronic form without requesting formal permission. Permission is required for any other external and/or commercial use. Requests for permission should be directed to the Systems Engineering Research Center at dschultz@stevens.edu * These restrictions do not apply to U.S. government entities. WHS TO 029, RT 044 UNCLASSIFIED Executive SummaryA major challenge to the successful planning and evolution of an Acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of a SoS are fully functioning, stand-alone systems in their own right; they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the So...

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“…In Ref. [8] the potential method is used in order to maneuver group behaviors such as formation, migration and obstacle avoidance in swarm systems. Finally, Refs.…”

Section: Role Of Artificial Potentialmentioning

confidence: 99%

Decentralized Control of a Swarm of Unmanned Aerial Vehicles

Ronchieri

Innocenti²,

Pollini³

et al. 2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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Several human operators control a single Unmanned Aerial Vehicle. This is not scalable. Recently, the trend is to have a single human operator to handle a group of Unmanned Aerial Vehicles in order to have a system able to work with thousands of Unmanned Aerial Vehicles flying over a country. Swarm Intelligence (the emergent, collective of social insect colonies) provides the guidelines to design such a decentralized system. In particular, social insects are capable of achieving several things, such as building and defending a nest, foraging for food, taking care of the brood, allocating labor, forming bridges.This thesis presents a framework for decentralized control of a swarm of Unmanned Aerial Vehicles based on the artificial potential functions characterized by attractive and repulsive properties, which are used respectively to achieve the goal and to avoid collisions. Each vehicle of the swarm makes use of limited information from others, and furthermore it is assumed to have a simple dynamic and to be identified as an agent. In this scheme, multiple agents in a swam are able to reach a configuration and to maintain it, while migrating as a group and avoiding collisions among each other. Therefore, the behaviors of the swarm system proposed in this thesis are group migration and configuration, and include collision avoidance.In particular, this thesis evaluates different potential expressions in order to determine how quickly the swarm converges to a desired direction and velocity, and how robust the swarm is against collisions among the agents. Furthermore, two metrics estimate which potential is the best one in a certain scenario. One quantifies how quickly the swarm converges to the given velocity, and the second evaluates how robust the potential is against collisions. The simulation results show that the proposed scheme can construct a swarm system with the capability of group migration and configuration in the presence of obstacles, by using a Non Equipaggiato -rendendo il sistema di controllo non scalabile. Attualmente, nell'ambito del controllo di questo tipo di veicoli, la tendenzaé quella di gestire un gruppo di Unmanned Aerial Vehicle tramite un solo operatore in modo da avere un sistema in grado di operare con migliaia di Unmanned Aerial Vehicle che volano sopra una nazione. Swarm Intelligence, basata sui cosiddetti insetti sociali, fornisce le linee guida per progettare sistemi decentralizzati. In particolare, gli insetti sociali sono in grado di perseguire diversi obiettivi, dalla costruzione e difesa del nido, alla ricerca del cibo, al prendersi cura del nido, all'assegnazione di squadre di operai, alla costruzione di ponti.Questa tesi presenta un framework per il controllo decentralizzato di uno sciame di Unmanned Aerial Vehicle basato su funzioni di potentiale artificiale caratterizzate da proprietá attrattive e repulsive, che sono usate rispettivamente per raggiungere l'obiettivo e per evitare le eventuali collisioni. Ciascun veicolo dello sciame utilizza un numero limitato di inf...

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A formation behavior for large-scale micro-robot force deployment

Cited by 41 publications

References 8 publications

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model: Phase 2

Decentralized Control of a Swarm of Unmanned Aerial Vehicles

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