Distributed robotic sensor networks: An information-theoretic approach

Julian, Brian J.; Angermann, Michael; Schwager, Mac; Rus, Daniela

doi:10.1177/0278364912452675

Cited by 147 publications

(103 citation statements)

References 34 publications

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“…Instead, the goal is often to maximize some measure of coverage or information [180], [181]. The robots in the team communicate over a multi-hop network and solve the surveillance task in a distributed fashion.…”

Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Assuming that the field under measure can be modeled as a Gaussian process (GP), researchers have proposed multiple criteria for predicting the "informativeness" of a sampling position, most commonly either the variance of the GP or mutual information. Mutual information has been shown to be a more effective measure of sample utility [2], and has subsequently been used for both sensor placement [3] and the control of mobile sensors [4], [5], [6]. Alternative methods have been proposed to better cope with a lack a priori information about the field under measure.…”

Section: Related Workmentioning

confidence: 99%

Environmental field estimation with hybrid-mobility sensor networks

Evans

Dias

Roelofsen

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 C over a 16 m 2 area ranging in value from 25-65 C.

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“…In their paper, the authors recommended the use of uncertainty in the absence of such true data. For a multi-robot case, Julian et al (2012) suggested an exploration strategy using MI. Their work solves for an optimal SLAM control strategy by evaluating the gradient of MI.…”

Section: Active Slammentioning

confidence: 99%

Active visual SLAM for robotic area coverage: Theory and experiment

Kim

Eustice

2014

The International Journal of Robotics Research

128

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This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area within a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM's navigation estimate will inevitably drift over time in the absence of loop-closures. Therefore, efficient area coverage and good SLAM navigation performance represent competing objectives. To solve this decision-making problem, we introduce perception-driven navigation, an integrated navigation algorithm that automatically balances between exploration and revisitation using a reward framework. This framework accounts for SLAM localization uncertainty, area coverage performance, and the identification of good candidate regions in the environment for visual perception. Results are shown for both a hybrid simulation and real-world demonstration of a visual SLAM system for autonomous underwater ship hull inspection.

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Distributed robotic sensor networks: An information-theoretic approach

Cited by 147 publications

References 34 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Environmental field estimation with hybrid-mobility sensor networks

Active visual SLAM for robotic area coverage: Theory and experiment

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