Brick&amp; Mortar: an on-line multi-agent exploration algorithm

Ferranti, Ettore; Trigoni, Niki; Levene, Mark

doi:10.1109/robot.2007.363078

Cited by 41 publications

(59 citation statements)

References 14 publications

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“…Ants [2], MDFS [3], and Brick&Mortar [3]). All of the tested approaches use an Agent-to-Tag communication paradigm, and assume an environment divided in a grid of square cells.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In both scenarios, the introduction of obstacles does not seem to slow down MDFS towards achieving both Exploration and Termination Objectives. In contrast, Brick&Mortar, which has a complex and time-consuming mechanism for resolving loops around obstacles [3], suffers from having to resolve an increasing number of obstacles. Furthermore, as one would expect, the plots denoting the exploration time of Brick&Mortar and MDFS cross over towards the middle of the x-axis, since Brick&Mortar is faster with few obstacles, but becomes inefficient with many obstacles.…”

Section: Simulation Resultsmentioning

confidence: 99%

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HybridExploration: A distributed approach to terrain exploration using mobile and fixed sensor nodes

Ferranti

Trigoni

Levene

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, longrange wireless communication is unreliable and nodes are not location-aware. We take into account these limitations, and propose a novel algorithm, HybridExploration, that makes use of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agentto-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agentto-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication.

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“…Ants [2], MDFS [3], and Brick&Mortar [3]). All of the tested approaches use an Agent-to-Tag communication paradigm, and assume an environment divided in a grid of square cells.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

HybridExploration: A distributed approach to terrain exploration using mobile and fixed sensor nodes

Ferranti

Trigoni

Levene

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Multiagent exploration problems are also well studied [15]. Ferranti et al [9] suggested an algorithm to make rapid exploration in small closed environment. They assume that robots communicate using tags in the environment.…”

Section: Multiagent Active Information Gatheringmentioning

confidence: 99%

Distributed Decision-Theoretic Active Perception for Multi-robot Active Information Gathering

Renoux¹,

Mouaddib²,

LeGloannec³

2014

Modeling Decisions for Artificial Intelligence

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Abstract. Multirobot systems have made tremendous progress in exploration and surveillance. However, information gathering tasks remain passive: the agents receive information from their sensors without acting in order to gather it. In this paper, we present a model and an algorithm for active information gathering using the information relevance concept. In this model, robots explore, assess the relevance, update their beliefs and communicate the appropriate information to relevant robots. To do so, we propose a distributed decision process where a robot maintains a belief matrix representing its beliefs and beliefs about the beliefs of the other robots. This decision process uses entropy and Hellinger distance in a reward function to access the relevance of their beliefs and their divergence with the other robots. This model allows to derive a policy for gathering information to make the entropy low and a communication policy to reduce the divergence between robot's beliefs. An experimental scenario has been developed for an indoor information gathering mission. Our model has been compared to two different systems : one without communication and one communicating each received observation. The results show that our approach is more efficient than both systems.

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“…Within our own group, a similarly decentralized coordination algorithm has been proposed [4], with a view to provide coordination opportunities in search and rescue operations conducted by robot teams. Similar to [8], this technique uses RFID tags embedded in the environment to relay information to other team members regarding the history of operation within a particular vicinity.…”

Section: Related Workmentioning

confidence: 99%

Shared Memories: A Trail-based Coordination Server for Robot Teams

Roussos¹,

Papadogkonas²,

Taylor³

et al. 2007

The Proceedings of First International Conference on Robot Communication and Coordination

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Abstract-Robust, dependable and concise coordination between members of a robot team is a critical ingredient of any such collective activity. Depending on the availability and the characteristics of the particular communication infrastructure, coordination mechanisms can take varied forms, leading to distinct system behaviors. In this paper, we consider the case of robot teams operating within relatively sparse wireless sensor network deployments. We introduce Shared Memories, a trailbased coordination engine, that analyzes interaction patterns between participating team members and sensor network nodes capable to discover significant aggregate patterns, which are made available to the team. To this end, we propose a model for the representation of captured interactions and their sensory context developed as a probabilistic grammar, as well as associated metrics used to rank trails and quantify their significance. Such trails are used as the basis for coordinated operation in team tasks and are made available by the engine to all team members. Our implementation deploys ad-hoc wireless local networking capability available through surrogate devices to commodity robots and RFID proximity sensors. We report on the performance of this system in experiments conducted in a laboratory environment, which highlight the advantages and limitations of our approach. I. INTRODUCTIONThe traditional robotics approach in capturing environmental information is one of self-sufficiency, that is each agent employs its own sensing capability to make sense of the environment around it and make task-related and navigation decisions. This mode of operation is challenged by two relatively recent developments: the wider availability of wireless adhoc networks which offer advanced networking opportunities; and the rapid proliferation of wireless sensor networks which establish a richer source of environmental information that can be employed to improve operational effectiveness. This recent shift in focus inevitably places greater importance in teams rather than individuals and the use of extended sensing capabilities as the enabler of new off-device functionalities. A critical challenge in capitalizing on these new developments is the management of the collective experience acquired in such a way, and the extraction of useful and usable knowledge.In this paper we propose a particular approach in organizing the collective information harvested by a robotic team and effective ways of providing cues for coordination employing a trail-based approach. We show within a simple feasibility study of this proposal how this approach can provide the foundation for effective coordinated operation. In addition to the use of trail records as the principle data primitive, our

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Brick& Mortar: an on-line multi-agent exploration algorithm

Cited by 41 publications

References 14 publications

HybridExploration: A distributed approach to terrain exploration using mobile and fixed sensor nodes

HybridExploration: A distributed approach to terrain exploration using mobile and fixed sensor nodes

Distributed Decision-Theoretic Active Perception for Multi-robot Active Information Gathering

Shared Memories: A Trail-based Coordination Server for Robot Teams

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