A Framework and Architecture for Multirobot Coordination

Alur, Rajeev; Das, Arindam; Esposito, Joel M.; Fierro, Rafael; Grudić, Gregory Z.; Hur, Yerang; Kumar, Vijay; Lee, I.; Ostrowski, James; Pappas, George J.; Southall, Ben; Spletzer, John; Taylor, Camillo J.

doi:10.1007/3-540-45118-8_31

Cited by 42 publications

(33 citation statements)

References 10 publications

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“…Classes, as well as models, have fields (variables they act on) and methods. 5 In Modelica, class methods are represented by equation and algorithm sections. An equation is syntactically defined as <expression = expression> and an equation section may contain a set of equations.…”

Section: Modelica Syntaxmentioning

confidence: 99%

“…Charon, an acronym for coordinated control, hierarchical design, analysis and run-time monitoring of hybrid systems, is a high-level language for modular specification of multiple, interacting hybrid systems developed at the University of Pennsylvania [3,4,5,8]. Charon is implemented and distributed in a toolkit that includes several tools for the specification, development, analysis and simulation of hybrid systems.…”

Section: Charonmentioning

confidence: 99%

“…State, input and output variables are declared by the type specifier (REAL for real-valued variables, or BOOL for Boolean-valued variables) that is followed by the variable name. 5 For real variables an optional interval can be specified by using the suffix [min,max] to denote the minimum and maximum value that the variable can assume, respectively. • PARAMETER: In the parameter subsection, a parameter can be specified in one of the following ways:…”

Section: Hysdel Syntaxmentioning

confidence: 99%

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Languages and Tools for Hybrid Systems Design

Carloni

Passerone

Pinto

et al. 2006

FNT in Electronic Design Automation

115

109

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The explosive growth of embedded electronics is bringing information and control systems of increasing complexity to every aspects of our lives. The most challenging designs are safety-critical systems, such as transportation systems (e.g., airplanes, cars, and trains), industrial plants and health care monitoring. The difficulties reside in accommodating constraints both on functionality and implementation. The correct behavior must be guaranteed under diverse states of the environment and potential failures; implementation has to meet cost, size, and power consumption requirements. The design is therefore subject to extensive mathematical analysis and simulation. However, traditional models of information systems do not interface well to the continuous evolving nature of the environment in which these devices operate. Thus, in practice, different mathematical representations have to be mixed to analyze the overall behavior of the system. Hybrid systems are a particular class of mixed models that focus on the combination of discrete and continuous subsystems. There is a wealth of tools and languages that have been proposed over the years to handle hybrid systems. However, each tool makes different assumptions on the environment, resulting in somewhat different notions of hybrid system. This makes it difficult to share information among tools. Thus, the community cannot maximally leverage the substantial amount of work that has been directed to this important topic. In this paper, we review and compare hybrid system tools by highlighting their differences in terms of their underlying semantics, expressive power and mathematical mechanisms. We conclude our review with a comparative summary, which suggests the need for a unifying approach to hybrid systems design. As a step in this direction, we make the case for a semantic-aware interchange format, which would enable the use of joint techniques, make a formal comparison between different approaches possible, and facilitate exporting and importing design representations.

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Section: Modelica Syntaxmentioning

confidence: 99%

Section: Charonmentioning

confidence: 99%

Section: Hysdel Syntaxmentioning

confidence: 99%

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Languages and Tools for Hybrid Systems Design

Carloni

Passerone

Pinto

et al. 2006

FNT in Electronic Design Automation

115

109

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“…Access to such actions can make tasks easier to solve, if they effectively solve subtasks within the domain. There are several approaches to defining such temporally-extended actions: options (Sutton et al, 1999), skills (Konidaris and Barto, 2009;Konidaris et al, 2010), activities (Barto and Mahadevan, 2003), modes (Alur et al, 2000), and behaviors (Huber and Grupen, 1997;Brooks, 1986).…”

Section: Challengementioning

confidence: 99%

Evolving multimodal behavior through modular multiobjective neuroevolution

Schrum

2015

SIGEVOlution

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Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it would be advantageous for artificial agents, both in physical and virtual worlds, to exhibit a similar diversity of behaviors. Artificial evolution, in particular neuroevolution [3, 4], is known to be capable of discovering complex agent behavior. This dissertation expands on existing neuroevolution methods, specifically NEAT (Neuro-Evolution of Augmenting Topologies [7]), to make the discovery of multiple modes of behavior possible. More specifically, it proposes four extensions: (1) multiobjective evolution, (2) sensors that are split up according to context, (3) modular neural network structures, and (4) fitness-based shaping. All of these technical contributions are incorporated into the software framework of Modular Multiobjective NEAT (MM-NEAT), which can be downloaded here.

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“…However in essence, it is similar to maintaing team leaders, which is a common practice in multirobot systems (e.g. [9]). …”

Section: Algorithmmentioning

confidence: 99%

A Distributed Biconnectivity Check

Ahmadi

Stone

Distributed Autonomous Robotic Systems 7

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Summary. For many distributed autonomous robotic systems, it is important to maintain communication connectivity among the robots. That is, each robot must be able to communicate with each other robot, perhaps through a series of other robots. Ideally, this property should be robust to the removal of any single robot from the system. In this work, we define a property of a team's communication graph that ensures this property, called biconnectivity. We present a distributed algorithm to check if a team of robots is biconnected, prove its correctness, and analyze it theoretically. IntroductionMany applications of distributed autonomous robotic systems can benefit from, or even may require, the team of robots staying within communication connectivity. For example, consider the problem of multirobot surveillance [1,2], in which a team of robots must collaboratively patrol a given area. If any two robots can directly communicate at all times, the robots can coordinate for efficient behavior. This condition holds trivially in environments that are smaller than the robots' communication range. However in larger environments, the robots must actively maintain physical locations such that any two robots can communicate -possibly through a series of other robots. Otherwise, the robots may lose track of each others' activities and become miscoordinated. Furthermore, since robots are relatively unreliable and/or may need to change tasks (for example if a robot is suddenly called by a human user to perform some other task), in a stable multirobot surveillance system, if one of the robots leaves or crashes, the rest should still be able to communicate. Some examples of other tasks that could benefit from any pair of robots being able to communicate with each other, are space and underwater exploration, search and rescue, and cleaning robots.We say that robot R 1 is connected to robot R 2 if there is a series of robots, each within communication range of the previous, which can pass a message from R 1 to R 2 . In order for the team to stay connected, it must be the case that every robot is connected to each other robot either directly or via two distinct paths that don't share any robots in common. We call this property biconnectivity: the removal of any one robot from the system does not disconnect the remaining robots from each other.In previous work, we developed algorithms for multirobot surveillance under the assumption that each pair of robots could communicate directly [2]. This communication assumption enabled the robots to negotiate to achieve an efficient task division,

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A Framework and Architecture for Multirobot Coordination

Abstract: Abstract

Cited by 42 publications

References 10 publications

Languages and Tools for Hybrid Systems Design

Languages and Tools for Hybrid Systems Design

Evolving multimodal behavior through modular multiobjective neuroevolution

A Distributed Biconnectivity Check

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