Cooperative air and ground surveillance

Grocholsky, Ben; Keller, James F.; Kumar, Vijay; Pappas, George J.

doi:10.1109/mra.2006.1678135

Cited by 432 publications

(214 citation statements)

References 13 publications

(21 reference statements)

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“…There have also been numerous works relating to cooperation between aerial and ground vehicles [13], [14], [15]. Our work is based on the general principles found in these -minimize the amount of information that has to be shared between platforms, maximize the amount of computing that can be performed in a distributed manner, and reduce the load on the operator.…”

Section: Related Workmentioning

confidence: 99%

3-D exploration with an air-ground robotic system

Butzkey

Dornbushy

Likhachevy

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Exploration of unknown environments is an important aspect to fielding teams of robots. Without the ability to determine on their own where to go in the environment, the full potential of robotic teams is limited to the abilities of human operators to deploy them for search and rescue, mapping, or other tasks that are predicated on gaining knowledge from the environment. This is of particular importance in realworld 3-Dimensional (3-D) environments where simple planar assumptions can lead to incomplete exploration, for example, real-world environments have areas underneath overhangs or inside caves. As an additional challenge, when the teams of robots have vastly different capabilities, the planning system must take those into account to efficiently utilize the available assets. In this paper, we present a combined air-ground system for conducting 3-D exploration in cluttered environments. We first describe the hardware and software components of the system. We then present our algorithm for planning 3-D goal locations for a heterogeneous team of robots to efficiently explore a previously unknown environment and demonstrate its applicability in real-world experiments.

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Section: Related Workmentioning

confidence: 99%

3-D exploration with an air-ground robotic system

Butzkey

Dornbushy

Likhachevy

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…However, that entropy describes the uncertainty over a certain location being reachable; it is not directly associated with the quality of the model nor is it linked with the statistics of measurement. Mutual information is used as a metric of significance of different discrete locations containing features or targets of interest, in an application where mobile robots and unmanned aerial vehicles (uavs) localize target features in their environment [24]. The Fisher information is another metric used to describe control actions for general information acquisition tasks [25] as well as motion coordination tasks [26].…”

Section: Entropy-based Approaches In Sensor Networkmentioning

confidence: 99%

Information Surfing for Radiation Map Building

Abdallah¹,

Cortez²,

Tanner³

et al. 2011

International Journal of Robotics and Automation

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We develop a control scheme for a group of mobile sensors to map radiation over a given planar polygonal region. The advantage of this methodology is that it provides quick situational awareness regarding radiation levels, which is being updated and refined in real-time as more measurements become available. The control algorithm is based on the concept of information surfing, where navigation is done by following information gradients, taking into account sensing performance and the dynamics of the observed process. It is tailored to cases of weak radioactivity, where source signals may be buried in background. We steer mobile sensors to locations which are critical points of a function that quantifies the information content of the measured signal, while * Corresponding author the time-asymptotic properties of the selected information metric facilitate the stability of the group motion. Information surfing allows for reactive mobile sensor network behavior and adaptation to environmental changes, as well as human retasking. Computer simulations and experiments are conducted to verify the asymptotic behavior of the robot group, and its distributed sensing and mapping capabilities.Key words: Sensor networks, cooperative control, radiation sensing MotivationThis work is motivated by the emerging threat of contamination from a malicious attack or accidental release of radioactive material. In such a case, a radiation map can be a valuable tool for rescue, response, and cleanup efforts. Although our focus is on an unexpected release of nuclear contaminants, our methods can also be applied to a wide range of other problems such as nuclear forensics and non proliferation, where robots could investigate the possibility that fissile material has been processed, by searching for small specks of that material.Existing technology in radiation detection is not well suited for the type of scenario described. Currently, searching for radiation sources is usually either done manually, by operators waving radiation counters in front of them as they walk, or by stationary portal monitors. The latter technology is used to detect radioactive sources in cargo or shipping containers at places such as ports of entry.Handheld radiation detectors do not provide any visual or statistical data map of the area in question. If human operators are used in the nuclear forensics problem, it is unlikely that their counters register any measurement at all, coming from a small amount of radioactive material or shielded special nuclear material (snm). Portal monitor systems on the other hand, while able to address the problem of scanning cargo for radioactive sources, lack the mobility needed when measurements are taken over a large area. Related ResearchExisting control design methods for multiple mobile robot coordination apply to problems ranging from topological mapping to formation and flocking tasks, as well as reconfigurable sensor networks. Sensor network deploymentReference [1] employs potential fields to reconfigure a mobile se...

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“…Further, a network of UASs is expected to improve the positioning accuracy (Roumeliotis and Bekey 2002) as well as aid those UASs which have lost access to the GNSS signal or are operating under GNSS challenged environments. Also a network of UASs can perform tasks such as search and rescue (Waharte et al 2009, Chung andBurdick 2008), exploration and mapping (Rekleitis et al 2003, Santamario et al), surveillance (Sutton et al 2008, Grocholsky et al 2006, Jones 2009), disaster management and situational awareness (Maza et al 2011, Kuntze et al 2012, exploration of unknown environments (Rekleitis et al 1997, Zheng et al 2005 etc. much more efficiently and much faster as compared to a single UAS.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Uas Localization Using Lowcost Sensors

Goel

Kealy

Lohani

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.

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Cooperative air and ground surveillance

Cited by 432 publications

References 13 publications

3-D exploration with an air-ground robotic system

3-D exploration with an air-ground robotic system

Information Surfing for Radiation Map Building

Cooperative Uas Localization Using Lowcost Sensors

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