An Autonomous Multi-UAV System for Search and Rescue

Scherer, Jürgen; Yahyanejad, Saeed; Hayat, Samira; Yanmaz, Evşen; Andre, Torsten; Khan, Asif; Vukadinović, Vladimir; Bettstetter, Christian; Hellwagner, Hermann; Rinner, Bernhard

doi:10.1145/2750675.2750683

Cited by 247 publications

(147 citation statements)

References 14 publications

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“…These targets can be stationary or mobile as well as collaborative or evasive [12,18,22,35,40,47]. Cameras have to be able to re-identify a target based on a given model.…”

Section: Current Practicementioning

confidence: 99%

The Future of Camera Networks

Esterle

Lewis

McBride³

et al. 2017

Proceedings of the 11th International Conference on Distributed Smart Cameras

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Camera networks become smart when they can interpret video data on board, in order to carry out tasks as a collective, such as target tracking and (re-)identification of objects of interest. Unlike today's deployments, which are mainly restricted to lab settings and highly controlled high-value applications, future smart camera networks will be messy and unpredictable. They will operate on a vast scale, drawing on mobile resources connected in networks structured in complex and changing ways. They will comprise heterogeneous and decentralised aggregations of visual sensors, which will come together in temporary alliances, in unforeseen and rapidly unfolding scenarios. The potential to include and harness citizen-contributed mobile streaming, body-worn video, and robotmounted cameras, alongside more traditional fixed or PTZ cameras, and supported by other non-visual sensors, leads to a number of difficult and important challenges. In this position paper, we discuss a variety of potential uses for such complex smart camera networks, and some of the challenges that arise when staying smart in the presence of such complexity. We present a general discussion on the challenges of heterogeneity, coordination, self-reconfigurability, mobility, and collaboration in camera networks. KEYWORDSfuture camera networks, surveillance, mobile smart cameras, body worn cameras, humans in the loop, distributed control ACM Reference format:

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“…These targets can be stationary or mobile as well as collaborative or evasive [12,18,22,35,40,47]. Cameras have to be able to re-identify a target based on a given model.…”

Section: Current Practicementioning

confidence: 99%

The Future of Camera Networks

Esterle

Lewis

McBride³

et al. 2017

Proceedings of the 11th International Conference on Distributed Smart Cameras

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“…It may be also decentralized [9], [13], where it is considered as a set of centralized networks. The distributed network, is considered as a solution for the problem of frailty of both previous networks due to links present over all nodes that makes a reliable communication [8], [18], [19]. The Ad Hoc network is an infrastructure less mode, also called mesh mode as we can confuse both designation.…”

Section: Related Workmentioning

confidence: 99%

“…The Ad Hoc network is an infrastructure less mode, also called mesh mode as we can confuse both designation. Some works use the 802.11s mesh network to perform a centralized system architecture with a distributed communication and centralized manner in case of failure of the first one where communication is able to satisfy some specific QoS, to reduce the traffic and to increase the response time and robustness [8]. The 802.11s mesh network may be used to connect two UAV and the 802.11g a link betweenthe UAV and the computer as mesh access point [4].…”

Section: Related Workmentioning

confidence: 99%

“…We need to ensure an efficient networking support, related to mission conditions, which can overcome communication losses, node failures and changes in network topology. In the literature, several works on Visual SLAM [2], [3] and on networking scheme for UAV communication [4], [5], [6], [7] have been presented, but it is rare to find studies on both topics together [8]. Therefore, our main purpose is to construct a distributed, and cooperative multi-UAVs visual SLAM system focusing on both communication and localization and mapping process to optimize the robot poses estimation and to improve the communication thus to obtain a reliable map.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-UAVs network communication study for distributed visual simultaneous localization and mapping

Mahdoui¹,

Natalizio²,

Frémont³

2016

2016 International Conference on Computing, Networking and Communications (ICNC)

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In this paper, the problem of multi-UAVs (Unmanned Arial Vehicles) Visual Simultaneous Localization and Mapping (SLAM) is considered by using a new framework for pose and map estimation using monocular vision and reduced communications capabilities. The problem of localization and mapping is solved by fusing monocular visual data with odometry measurements through Graph SLAM formulation. Using each robot's map data representation, a proposal is made for a good and robust communication between UAVs to perform efficient data exchange while keeping SLAM performances. A mesh network is chosen to import solutions to wireless networking. Finally, some validation experiments are performed in an Ad Hoc Network and a Wireless Mesh Network using Better Approach To Mobile Ad Hoc Network (BATMAN) protocol.

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“…M OBILE robots, especially unmanned aerial vehicles (UAVs), are of increasing interest in various application domains including surveillance. Examples for the deployment of robots for surveillance scenarios are disaster response [1], [2], [3], wildfire monitoring [4], security tasks [5], environmental monitoring [6], and exploration and mapping [7], [8]. Persistent surveillance is the task of continuously monitoring an environment over a longer period of time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Persistent Surveillance With Connectivity Constraints

Scherer

Rinner

2020

IEEE Access

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Mobile robots, especially unmanned aerial vehicles (UAVs), are of increasing interest for surveillance and disaster response scenarios. We consider the problem of multi-robot persistent surveillance with connectivity constraints where robots have to visit sensing locations periodically and maintain a multihop connection to a base station. We formally define several problem instances closely related to multi-robot persistent surveillance with connectivity constraints, i.e., connectivity-constrained multirobot persistent surveillance (CMPS), connectivity-constrained multi-robot reachability (CMR), and connectivity-constrained multi-robot reachability with relay dropping (CMRD), and show that they are all NP-hard on general graph. We introduce three heuristics with different planning horizons for convex grid graphs and combine these with a tree traversal approach which can be applied to a partitioning of non-convex grid graphs (CMPS with tree traversal, CMPSTT). In simulation studies we show that a short horizon greedy approach, which requires parameters to be optimized beforehand, can outperform a full horizon approach, which requires a tour through all sensing locations, if the number of robots is larger than the minimum number of robots required to reach all sensing locations. The minimum number required is the number of robots necessary for building a chain to the farthest sensing location from the base station. Furthermore, we show that partitioning the area and applying the tree traversal approach can achieve a performance similar to the unpartitioned case up to a certain number of robots but requires less optimization time.

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An Autonomous Multi-UAV System for Search and Rescue

Cited by 247 publications

References 14 publications

The Future of Camera Networks

The Future of Camera Networks

Multi-UAVs network communication study for distributed visual simultaneous localization and mapping

Multi-Robot Persistent Surveillance With Connectivity Constraints

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