Adaptive communication in multi-robot systems using directionality of signal strength

Gil, Stephanie; Kumar, Swarun; Katabi, Dina; Rus, Daniela

doi:10.1177/0278364914567793

Cited by 51 publications

(16 citation statements)

References 40 publications

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“…For example, the resources for communications, e.g., transmit power and bandwidth, are limited and the channel quality is time-varying. When the channel is in deep fading, received data suffer from severe errors [84] and noticeable delay is also inevitable. As a result, developing efficient coordination and cooperation schemes for multiple vehicle agents while taking the wireless constraints into consideration needs to be further explored.…”

Section: B Distributed Learning and Multi-agent Cooperationmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

229

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As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

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Section: B Distributed Learning and Multi-agent Cooperationmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

229

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“…As mentioned earlier, the main goals of robotic router placements are to fulfill certain communication requirements such as supporting a set of flows [174], guaranteeing certain performance criterion (say, data rate) to the customers [175], or fixing holes [176]. In [174], Williams, Gasparri, and Krishnamachari presented a hybrid architecture called INSPIRE, with two separate planes called Physical Control Plane (PCP) and Information Control Plane (ICP).…”

Section: Robotic Router Positioningmentioning

confidence: 99%

“…They used Particle Swarm Optimization (PSO) [178] for finding the optimal configuration due to non-convexity of the problem. Gil et al [175], also proposed a method of robotic router placements where the communication demands (in terms of data rates) of a set of clients are fulfilled by another set of robotic routers. The demands are modeled in terms of effective SNR (ESNR) to represent the required rate.…”

Section: Robotic Router Positioningmentioning

confidence: 99%

Robotic Wireless Sensor Networks

Ghosh

Gasparri

Jin

et al. 2019

Mission-Oriented Sensor Networks and Systems: Art and Science

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In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according

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“…Clearly, this technique does not solve our synchronization problem due to the meetings being fortuitous. [14] consider the problem of satisfying communication demands in a multiagent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. The goal is to position the team of robotic routers to provide communication coverage to the remaining client robots.…”

Section: Related Workmentioning

confidence: 99%

A General Framework for Synchronizing a Team of Robots Under Communication Constraints

et al. 2017

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This paper addresses a synchronization problem that arises when a team of aerial robots (ARs) need to communicate while performing assigned tasks in a cooperative scenario. Each robot has a limited communication range and flies within a previously assigned closed trajectory. When two robots are close enough, a communication link may be established, allowing the robots to exchange information. The goal is to schedule the flights such that the entire system can be synchronized for maximum information exchange, that is, every pair of neighbors always visit the feasible communication link at the same time. We propose an algorithm for scheduling a team of robots in this scenario and propose a robust framework in which the synchronization of a large team of robots is assured. The approach allows us to design a fault-tolerant system that can be used for multiple tasks such as surveillance, area exploration, searching for targets in a hazardous environment, and assembly and structure construction, to name a few.

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Adaptive communication in multi-robot systems using directionality of signal strength

Cited by 51 publications

References 40 publications

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Robotic Wireless Sensor Networks

A General Framework for Synchronizing a Team of Robots Under Communication Constraints

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