Robots for Environmental Monitoring: Significant Advancements and Applications

Dunbabin, Matthew; Marques, Lino

doi:10.1109/mra.2011.2181683

Cited by 520 publications

(277 citation statements)

References 84 publications

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“…It has become clear that robotic vehicles are especially useful for monitoring in remote and hostile environments and that their potential is much enhanced when they perform in cooperative teams [100]. A recent survey of robotics for environmental monitoring, including a discussion of cooperative systems, is provided by Dunbabin and Marques [101]. Redfield surveys works on cooperative underwater vehicles in [102].…”

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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Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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“…It should be mentioned that the environment in this model (and throughout this paper) is presented by uniform grid maps, so any Cartesian (x, y) denotes a grid cell with center at (x, y). Another point is that since this equation presents a probability function, its result is truncated to [0,1]. Although this equation has been simplified by considering z = constant = z 0 , standard deviations of vertical direction (σ z (x)) exists and plays a significant role in this probability function.…”

Section: B Gas Sensor Area Coveragementioning

confidence: 99%

“…This problem finds applications in environmental monitoring [1], chemical leak detection [2], pollution monitoring [3], inspection of landfills [4], and search and rescue operations [5]. Some of these tasks are done in scenarios extremely dangerous for humans, being desirable to use robots instead.…”

Section: Introductionmentioning

confidence: 99%

Optimal Swarm Formation for Odor Plume Finding

Marjovi

Marques

2014

IEEE Trans. Cybern.

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Abstract-This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

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“…To deal with delays, Lyapunov-Krasovskii functionals and inequality techniques are used in [16] and [25], respectively. In addition, multiagent surveillance and distributed environmental monitoring are investigated in [22], [21] and [5]. In [4], with the help of Voronoi partitions, Cortés et al study the coverage control problem for sensor networks.…”

Section: Introductionmentioning

confidence: 99%