Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks

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

doi:10.1109/jproc.2011.2158377

Cited by 196 publications

(131 citation statements)

References 27 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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“…Commonly used objectives in such patrol problem formulations include minimizing the average or worst case revisit rate to return to a given location, which has correspondence to measures of service rates and wait time in queuing theory models [7,12]. Other metrics, such as maximized area coverage for sensor deployments [13,2], enable decentralized control laws to govern persistent surveillance of areas. However, these previous models do not incorporate the possibility of imperfect detections of the events, for which Bayesian methods found in probabilistic search theory [6,5] provide key insights.…”

Section: Related Workmentioning

confidence: 99%

Distributed Online Patrolling with Multi-agent Teams of Sentinels and Searchers

Basilico

Chung

Carpin

2016

Springer Tracts in Advanced Robotics

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We consider the problem of patrolling an assigned area using a team of heterogeneous robots consisting of sentinels and searchers in the presence of stochastic arrivals of attacks. Sentinels and searchers operate using a different sensor model featuring a tradeoff between accuracy and the sensed area. Using an approach based on queuing theory, we derive an accurate analytic characterization of the patrolling performance that can be used to predict the behavior of a given configuration or inform the composition of a team in order to meet a desired target performance. Extensive simulation results corroborate our theoretical findings.

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“…Assuming availability of standard trajectory tracking controllers [6,16], we model the closed-loop quadrotor behavior as a single integrator in R 3 , see also [10,11,28] for similar working assumptions. As explained in Section II-A, here, for the sake of illustration, we consider tridimensional agents (and related tridimensional version of the rigidity maintenance controller (12)), although the previous developments have been worked out only for the planar case.…”

Section: Simulationsmentioning

confidence: 99%

Rigidity Maintenance Control for Multi-Robot Systems

Zelazo

Franchi

Allgöwer

et al. 2012

Robotics: Science and Systems VIII

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Abstract-Rigidity of formations in multi-robot systems is important for formation control, localization, and sensor fusion. This work proposes a rigidity maintenance gradient controller for a multi-agent robot team. To develop such a controller, we first provide an alternative characterization of the rigidity matrix and use that to introduce the novel concept of the rigidity eigenvalue. We provide a necessary and sufficient condition relating the positivity of the rigidity eigenvalue to the rigidity of the formation. The rigidity maintenance controller is based on the gradient of the rigidity eigenvalue with respect to each robot position. This gradient has a naturally distributed structure, and is thus amenable to a distributed implementation. Additional requirements such as obstacle and inter-agent collision avoidance, as well as typical constraints such as limited sensing/communication ranges and line-of-sight occlusions, are also explicitly considered. Finally, we present a simulation with a group of seven quadrotor UAVs to demonstrate and validate the theoretical results.

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Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks

Cited by 196 publications

References 27 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Distributed Online Patrolling with Multi-agent Teams of Sentinels and Searchers

Rigidity Maintenance Control for Multi-Robot Systems

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