A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

Merino, Luís; Caballero, Fernando; Dios, J.R. Martínez-de; Ferruz, J.; Ollero, A.

doi:10.1002/rob.20108

Cited by 257 publications

(128 citation statements)

References 21 publications

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“…Intellectualization AI is a key technology for future UAV systems to improve their autonomous performance. Intellectualization of UAVs is occurring mainly in terms of autonomous flight path planning ability (Rathbun et al, 2002;Tisdale et al, 2009), autonomous decision-making ability for tasks (Ren et al, 2010), and autonomous air fleet collaboration ability (Merino et al, 2006;Maza et al, 2010). Among these abilities, autonomous path planning is the first intelligent trend in UAVs.…”

Section: Trends In Unmanned Aerial Vehicle Developmentmentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

134

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Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

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Section: Trends In Unmanned Aerial Vehicle Developmentmentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

134

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“…Hollinger et al [108] demonstrated on a single AUV in the Southern California Bight a probabilistic planner that uses uncertainty in ocean current prediction based on an interpolation variance. Merino et al [109] presented a cooperative perception system for multiple UAVs with different kinds of sensors and showed experimental results of forest fire detection with cooperating UAVs. Alvarez et al [110] described methodology that estimates volumetric distribution of the geostrophic current field from glider measurements merged with satellite altimetry data; this methodology was validated using data collected from three Slocum gliders and one Spray glider moving along predefined paths during a field experiment in August 2010 in a coastal region of the Ligurian Sea.…”

Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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“…This work contrasts with more traditional work on unmanned aerial vehicle (UAV) coordination where a number of (usually fixed wing) air vehicles are used to search a larger (non urban) area for specific threats such as fires (Merino et al, 2006). Fixed wing UAVs require some care in reconciling the limits of their Dubin paths, with the need to automatically track specific targets, and path optimisation based on Bayesian methods or mutual information has proved to be successful in these instances (Cole et al, 2009;How et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A multiple robot solution for urban reconnaissance

Janko

Turner

Cave-Ayland

et al. 2011

IJIDSS

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Coordinating aerial and ground based robotic platforms together with sensor payloads is described and some results are presented on the deployment of these robots in an urban environment for reconnaissance, in particular to find military threats. This work was done with the aim of competing in the UK MOD Grand Challenge competition in August 2008. This paper reviews the structure, control, coordination and integration of the robotic platforms for this event.

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A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

Cited by 257 publications

References 21 publications

Current trends in the development of intelligent unmanned autonomous systems

Current trends in the development of intelligent unmanned autonomous systems

Cooperative Vehicle Environmental Monitoring

A multiple robot solution for urban reconnaissance

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