Utility-based on-line exploration for repeated navigation in an embedded graph

Argamon-Engelson, Shlomo; Kraus, Sarit; Sina, Sigalit

doi:10.1016/s0004-3702(98)00014-9

Cited by 5 publications

(7 citation statements)

References 10 publications

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“…The main objective of these algorithms is to reduce the uncertainty of new routes to traverse an area. To the best of our knowledge, there are only three more works that deal with the restrictions proposed by Betke et al [16]: Argamon et al [18], Duncan et al [17], P. B. Sujit and Debasish Ghose [19]. Moreover, in opportunistic routing, Shah et al [10] presented another work that can be extended to satisfy the PSM constraints.…”

Section: Constrained Explorationmentioning

confidence: 99%

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Data-Foraging-Oriented Reconnaissance Based on Bio-Inspired Indirect Communication for Aerial Vehicles

et al. 2017

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Abstract:In recent years, aerial vehicles have allowed exploring scenarios with harsh conditions. These can conduct reconnaissance tasks in areas that change periodically and have a high spatial and temporal resolution. The objective of a reconnaissance task is to survey an area and retrieve strategic information. The aerial vehicles, however, have inherent constraints in terms of energy and transmission range due to their mobility. Despite these constraints, the Data Foraging problem requires the aerial vehicles to exchange information about profitable data sources. In Data Foraging, establishing a single path is not viable because of dynamic conditions of the environment. Thus, reconnaissance must be focused on periodically searching profitable environmental data sources, as some animals perform foraging. In this work, a data-foraging-oriented reconnaissance algorithm based on bio-inspired indirect communication for aerial vehicles is presented. The approach establishes several paths that overlap to identify valuable data sources. Inspired by the stigmergy principle, the aerial vehicles indirectly communicate through artificial pheromones. The aerial vehicles traverse the environment using a heuristic algorithm that uses the artificial pheromones as feedback. The solution is formally defined and mathematically evaluated. In addition, we show the viability of the algorithm by simulations which have been tested through various statistical hypothesis.

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Section: Constrained Explorationmentioning

confidence: 99%

“…Argamon et al [18] present an on-line exploration while performing algorithm for a repeated task(s). A repeated task must be done continuously, more than once.…”

Section: Constrained Explorationmentioning

confidence: 99%

Data-Foraging-Oriented Reconnaissance Based on Bio-Inspired Indirect Communication for Aerial Vehicles

et al. 2017

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“…Otherwise, the agent chooses to explore the shortcut with the highest utility. This utility function defined below was first presented by [2,1] for a single agent.…”

Section: Utility-based Algorithmmentioning

confidence: 99%

“…Argamon et al [2,1] addressed this problem for a single mobile agent. They showed that in such cases, it is more effective to interleave exploration of the environment during the actual performance of the task.…”

Section: Introductionmentioning

confidence: 99%

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Utility-based multi-agent system for performing repeated navigation tasks

Meshulam

Felner

Kraus

2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

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Suppose that a number of mobile agents need to travel back and forth between two locations in an unknown environment a given number of times. These agents need to find the right balance between exploration of the environment and performing the actual task via a known suboptimal path. Each agent should decide whether to follow the best known path or to devote its effort for further exploration of the graph so as to improve the path for future usage. We introduce a utility-based approach which chooses its next job such that the estimation of global utility is maximized. We compare this approach to a stochastic greedy approach which chooses its next job randomaly so as to increase the diversity of the known graph. We apply these approaches to different environments and to different communication paradigms. Experimental results show that an intelligent utility-based multi-agent system outperforms a stochastic greedy multi-agent system. In addition the utility-based approach was robust under inaccurate input and limitation of the communication abilities.

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Efficient learning of variable-resolution cognitive maps for autonomous indoor navigation

Arleo

Millán

Floreano

1999

IEEE Trans. Robot. Automat.

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This paper presents an adaptive method that allows mobile robots to learn cognitive maps of indoor environments incrementally and on-line. Our approach models the environment by means of a variable-resolution partitioning that discretizes the world in perceptually homogeneous regions. The resulting model incorporates both a compact geometrical representation of the environment and a topological map of the spatial relationships between its obstacle-free areas. The efficiency of the learning process is based on the use of local memory-based techniques for partitioning and of active learning techniques for selecting the most appropriate region to be explored next. In addition, a feed-forward neural network is used to interpret sensor readings. We present experimental results obtained with two different mobile robots, namely a Nomad 200 and a Khepera. The current implementation of the method relies on the assumption that obstacles are parallel or perpendicular to each other. This results in variable-resolution partitionings consisting of simple rectangular partitions and reduces the complexity of treating the underlying geometrical properties.

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Utility-based on-line exploration for repeated navigation in an embedded graph

Cited by 5 publications

References 10 publications

Data-Foraging-Oriented Reconnaissance Based on Bio-Inspired Indirect Communication for Aerial Vehicles

Data-Foraging-Oriented Reconnaissance Based on Bio-Inspired Indirect Communication for Aerial Vehicles

Utility-based multi-agent system for performing repeated navigation tasks

Efficient learning of variable-resolution cognitive maps for autonomous indoor navigation

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