Cooperative &amp;#x03BC;UAV-UGV autonomous indoor surveillance

Saska, Martin; Krajník, Tomáš; Pfeucil, Libor

doi:10.1109/ssd.2012.6198051

Cited by 52 publications

(30 citation statements)

References 10 publications

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“…Beside the GeNav leader, we assume to use a group of simple UGV followers without any on-board sensors for their localization and a group of MAV followers (quadrotors) equipped with a bottom camera and a system for visual relative localization [3]. This system provides information on the relative position between the camera of MAVs and center of an identification pattern.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…The deviation of the system (virtual leader or a follower) from its desired position is penalized only by the first term of function (3). The functional value of this term corresponds not only to the actual deviation of the system, but it characterizes also its progress in future.…”

Section: A Failure Detectionmentioning

confidence: 99%

“…The obstacles force the robot to leave the desired position in the formation. The deviation from the desired state is penalized by the first term in the equation (3). The red lines in the courses of the values denote parts, in which the GeNav leader is turning to be able to follow the next path segment and the rest of the formation is waiting in static positions.…”

Section: A Simulation Of the Formation Movement With Obstacle Avoidamentioning

confidence: 99%

“…This paper is focussed on theoretical and technical aspects of the formation driving mechanism suited for the real-world deployment of autonomous robots under the GeNav navigation and the top-view localization, while technical details on the visual relative localization are available in [3] and the GeNav navigation in [2]. In addition, the paper addresses issues of the fault identification and recovery to increase robustness of the method.…”

Section: Introductionmentioning

confidence: 99%

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Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Saska

Krajník

Vonásek

et al. 2013

J Intell Robot Syst

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Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Section: A Failure Detectionmentioning

confidence: 99%

Section: A Simulation Of the Formation Movement With Obstacle Avoidamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Saska

Krajník

Vonásek

et al. 2013

J Intell Robot Syst

Self Cite

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“…The system has been already deployed in a number of international mobile robotics projects concerning distributed quad rotor localization [17], visual based autonomous navigation [18], decentralized formation control [19], evolutionary swarm [20] and educational [15] robotics. Since the system has already proved to be useful in a variety of applications, we publish its source code [21]; so, other roboteers can use it for their projects.…”

Section: Introductionmentioning

confidence: 99%

External localization system for mobile robotics

Krajník

Nitsche

Faigl

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

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Abstract-We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

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Radiation search operations using scene understanding with autonomous UAV and UGV

Christie

Shoemaker

Kochersberger

et al. 2017

Journal of Field Robotics

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Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.

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Cooperative μUAV-UGV autonomous indoor surveillance

Cited by 52 publications

References 10 publications

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

External localization system for mobile robotics

Radiation search operations using scene understanding with autonomous UAV and UGV

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