Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Pack, Daniel J.; DeLima, Pedro; Toussaint, Gregory J.; York, George

doi:10.1109/tsmcb.2009.2010865

Cited by 39 publications

(63 citation statements)

References 20 publications

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“…In [23], mobile targets with intermittent transmission are localized using a state machine to switch between the following four states: 1) global search, 2) approaching target, 3) locating target, and 4) target reacquisition. The cooperation is implemented in a decentralized fashion via 1) a cost function that takes into account the distance that UAV needs to travel to arrive at a location in which it can help the localization, 2) the number of currently helping UAVs at that location, and 3) the number of neighboring UAVs.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

2018

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Localization of a radio frequency (RF) transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength (RSS) sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization), and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare steepest descent posterior Cramer-Rao lower bound (CRLB) path planning and a bio-inspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bioinspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection based EKF.

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Section: Introductionmentioning

confidence: 99%

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

2018

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“…The multi-robot hunting is even more challenging and complex problem in multirobot cooperation systems as it includes all such subproblems. There is the number of solutions presented to solve the problem of multi-robot based cooperative hunting [15][16][17]. Such conventional methods classified into two categories such as location-based and sensorbased methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Algorithm Design for Cooperative Hunting in Multi-Robots

Agrawal¹,

Agrawal²

2018

IJISA

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The multi-robot cooperative planning is gained significant attention in recent past mainly for the evaders hunting task. In evaders hunting, the robot nodes required to recognize their other team members and considering their current positions and capabilities to catch the stationary or moving evaders effectively through the cooperating path planning approach. The primary challenge to design cooperative multi-robot evader hunting system is efficient and adaptive coordination of multiple autonomous mobile robots with less delay and communication overhead in presence of big-size obstacles. The current solutions suffered from repeated hunting problem under the inaccessible network conditions due to the presence of big-size obstacles and ineffective utilization of known nodes information. In this paper, to alleviate the problem of repeated hunting and inefficient catching of all evaders in the network, we proposed the adaptive Bio-inspired Neural Network (ABNN) using the new shunting equation with the capability of adaptive hunting of all evaders in the system. We design ABNN based on the implicit robot to predict the next path to catch evaders efficiently by real robots. The use of implicit robot helps to prevent the big sized evaders and efficiently utilize the evader's information. The simulation results demonstrate that ABNN performs efficient evaders hunting under the presence of big size obstacles.Index Terms-Multi-robots, bio-inspired neural network, evaders, cooperative hunting problem and implicit robot.

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“…4,5 These modules could be used along with aerial robots for improving the search speed and performance. [6][7][8] As this procedure suffers from the fact that only a low number of people at risk own such emitters, some recent works focus on the idea of localizing cellphones and other more common handheld devices. 9,10 The use of airborne radar instruments for locating snow avalanche victims was investigated in Ref.…”

Section: Introductionmentioning

confidence: 99%

Localization of emergency acoustic sources by micro aerial vehicles

Basiri

Schill

Lima

et al. 2017

Journal of Field Robotics

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For micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV‐based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robot's attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real‐world experiments with real flying robots.

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Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Cited by 39 publications

References 20 publications

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Adaptive Algorithm Design for Cooperative Hunting in Multi-Robots

Localization of emergency acoustic sources by micro aerial vehicles

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