Authority Sharing in a Swarm of UAVs: Simulation and Experiments with Operators

Legras, François; Glad, Arnaud; Simonin, Olivier; Charpillet, François

doi:10.1007/978-3-540-89076-8_29

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…It is also possible to follow a design based on “collective” interaction, in which entities are not aware of other entities in the team, yet they do share goals, and their actions are beneficial to their teammates (Parker, 2008). Although swarm approaches have been studied for surveillance/tracking tasks with UAVs (Legras, Glad, Simonin, & Charpillet, 2008), this option was not adopted because it did not fit well with the requirements of the end users.…”

Section: Introductionmentioning

confidence: 99%

A distributed architecture for a robotic platform with aerial sensor transportation and self‐deployment capabilities

Maza

Caballero

Capitán

et al. 2011

Journal of Field Robotics

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This paper presents the architecture developed in the framework of the AWARE project for the autonomous distributed cooperation between unmanned aerial vehicles (UAVs), wireless sensor/actuator networks and ground camera networks. One of the main goals was the demonstration of useful actuation capabilities involving multiple ground and aerial robots in the context of civil applications. A novel characteristic is the demonstration in field experiments of the transportation and deployment of the same load with single/multiple autonomous aerial vehicles.The architecture is endowed with different modules that solve the usual problems that arise during the execution of multi-purpose missions, such as task allocation, conflict resolution, task decomposition and sensor data fusion. The approach had to satisfy two main requirements: robustness for the operation in disaster management scenarios and easy integration of different autonomous vehicles. The former specification led to a distributed design and the latter was tackled by imposing several requirements on the execution capabilities of the vehicles to be integrated in the platform.

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Section: Introductionmentioning

confidence: 99%

A distributed architecture for a robotic platform with aerial sensor transportation and self‐deployment capabilities

Maza

Caballero

Capitán

et al. 2011

Journal of Field Robotics

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“…Machine learning has been used to deal with different issues in UAV research and development. For example, Berger et al [2] use a co-evolutionary algorithm for information gathering in UAV teams; Allaire et al [1] have used genetic algorithms for UAV real-time path planning; and Sauter et al [7,5] have used a swarming approach (for which a ground sensor network for coordination purposes is needed).…”

Section: Introductionmentioning

confidence: 99%

Online Adaptation of Path Formation in UAV Search-and-Identify Missions

Willigen

Schut²,

Eiben³

et al. 2011

Adaptive and Natural Computing Algorithms

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Abstract. In this paper, we propose a technique for optimisation and online adaptation of search paths of unmanned aerial vehicles (UAVs) in search-and-identify missions. In these missions, a UAV has the objective to search for targets and to identify those. We extend earlier work that was restricted to offline generation of search paths by enabling the UAVs to adapt the search path online (i.e., at runtime). We let the UAV start with a pre-planned search path, generated by a Particle Swarm Optimiser, and adapt it at runtime based on expected value of information that can be acquired in the remainder of the mission. We show experimental results from 3 different types of UAV agents: two benchmark agents (one without any online adaptation that we call 'naive' and one with predefined online behaviour that we call 'exhaustive') and one with adaptive online behaviour, that we call 'adaptive'. Our results show that the adaptive UAV agent outperforms both the benchmarks, in terms of jointly optimising the search and identify objectives.Keywords: adaptive algorithm; design and engineering for self-adaptive systems; unmanned aerial vehicles; search and identify.

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