Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance

Saska, Martin; Chudoba, J.; Precil, Libor; Thomas, Justin; Loianno, Giuseppe; Tresnak, Adam; Vonásek, Vojtĕch; Kumar, Vijay

doi:10.1109/icuas.2014.6842301

Cited by 58 publications

(28 citation statements)

References 18 publications

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“…This section is a summary of the approach originally published by our team in the conference paper [55]. Here, the description of the method is put into the context of the presented control and localization system and it is used as an example of the system deployment in scenarios, where the trajectories of the robots have to be purposely computed prior the mission for their verification by the operator.…”

Section: B Scenariomentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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Section: B Scenariomentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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“…The UAVs keep a certain pattern and track a RFT. This type of formation has applications such as object transportation [4] and surveillance of area of interest [15].…”

Section: A L-f Consensus Of Quadrotorsmentioning

confidence: 99%

“…The desired attitude angles for each UAV are obtained by substituting (15) and (23) into (12). Owing to the use of the hyperbolic tangent function, u (13) caused by the tracking error of the attitude angles.…”

Section: Proposition 4 the Formation Controller U I (15) Is Boundedmentioning

confidence: 99%

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Interactive Leader–Follower Consensus of Multiple Quadrotors Based on Composite Nonlinear Feedback Control

Hou

Fantoni

2018

IEEE Trans. Contr. Syst. Technol.

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“…It is fundamental for autonomous robotics that can operate in unknown environments in the real world, such as self-driving cars and UAVs, and can open the way to exciting applications. Existing work includes using swarms for rapid exploration of large spaces [1], much more effective search and rescue operations [2] and precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Semi-dense SLAM on an FPGA SoC

Boikos

Bouganis

2016

2016 26th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-Deploying advanced Simultaneous Localisation and Mapping, or SLAM, algorithms in autonomous low-power robotics will enable emerging new applications which require an accurate and information rich reconstruction of the environment. This has not been achieved so far because accuracy and dense 3D reconstruction come with a high computational complexity. This paper discusses custom hardware design on a novel platform for embedded SLAM, an FPGA-SoC, combining an embedded CPU and programmable logic on the same chip. The use of programmable logic, tightly integrated with an efficient multicore embedded CPU stands to provide an effective solution to this problem. In this work an average framerate of more than 4 frames/second for a resolution of 320x240 has been achieved with an estimated power of less than 1 Watt for the custom hardware. In comparison to the software-only version, running on a dualcore ARM processor, an acceleration of 2× has been achieved for LSD-SLAM, without any compromise in the quality of the result.

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Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance

Cited by 58 publications

References 18 publications

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Interactive Leader–Follower Consensus of Multiple Quadrotors Based on Composite Nonlinear Feedback Control

Semi-dense SLAM on an FPGA SoC

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