A decentralized approach for multi-UAV multitarget tracking and surveillance

Adamey, Emrah; Özgüner, Ü.

doi:10.1117/12.918975

Cited by 18 publications

(11 citation statements)

References 16 publications

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“…v=HDbNXdvlG1g. The existing decentralized path-planning methods for a multi-UAV system include the artificial potential field [26], the consensus-based algorithm [27], decentralized overlapping control [28], DMPC [29] and so on. With the advantages of low computation load, good flexibility and strong capability when dealing with environmental uncertainty, DMPC has been widely studied in relation to the applications of multi-UAVs, such as conflict-free trajectory planning [29], formation flight control [30], encirclement of a target [31] etc.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Yang

Fang

2016

International Journal of Advanced Robotic Systems

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A decentralized four-dimensional (4D) trajectory generation method for unmanned aerial vehicles (UAVs), which uses the improved intrinsic tau gravity (tau-G) guidance strategy, is presented in this paper. Based on general tau theory, the current tau-G strategy can only generate the 4D trajectory with zero initial and final velocities, which is not appropriate for decentralized applications. By adding an initial velocity to the intrinsic movement of tau-G strategy, the improved tau-G strategy can synchronously guide the position and velocity to the desired values at the arrival time. In our decentralized 4D trajectory generation method, the improved tau-G strategy is used to plan the 4D trajectories for UAVs. To deal with environmental uncertainty and communication limitations, the receding horizon optimization driven by both sampling time and conflict events is utilized to renew trajectory parameters continually. The simulation results of challenging time-constrained tasks demonstrate that the proposed method can efficiently provide safer and lower-cost 4D trajectories.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Yang

Fang

2016

International Journal of Advanced Robotic Systems

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“…2; a video is available online 1 . While the controls selected by the approach were quite smooth, the zigzag movements of the robots were due to the simulated motion noise.…”

Section: Results and Insightsmentioning

confidence: 99%

“…Our probabilistic method for cooperative target tracking aims at finding the joint controls u = [u (1) , . .…”

Section: Optimization-based Control and Topology Switchingmentioning

confidence: 99%

“…For cooperative target tracking with onboard sensors, many authors considered centralized [5,6,18], decentralized [1,15,17], and distributed [10,11,21] approaches to multi-robot control in aerial and ground settings. However, these methods estimate the pose of the target and assume that the poses of the robots are known, e.g., from an external system or by reference to a global map.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Control for Target Tracking with Onboard Sensing

Hausman

Müller

Hariharan

et al. 2015

Experimental Robotics

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We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not assume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into account the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. It reasons about multiple possible sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations show the performance of our approach and prove its flexibility to find suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment.

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“…In the domain of cooperative control, small unmanned aerial vehicles (UAVs) have recently become prominent, and several well-constructed testbeds have been established for multi-robot control and aerobatics with motion capture state estimates (Lupashin et al, 2010;Michael et al, 2010;Valenti et al, 2006). For cooperative target tracking with onboard sensors, many authors considered centralized (Charrow et al, 2013;Fink et al, 2010), decentralized (Adamey and Ozguner, 2012;Lima et al, 2014;Mottaghi and Vaughan, 2006;Ong et al, 2006), and distributed (Jung and Sukhatme, , 2006Wang and Gu, 2012) approaches to multi-robot control in aerial and ground settings. Lima et al (2014) reduce the uncertainty of the tracking target while keeping robots in the pre-set formation.…”

Section: Related Workmentioning

confidence: 99%

Cooperative multi-robot control for target tracking with onboard sensing

Hausman

Müller

Hariharan

et al. 2015

The International Journal of Robotics Research

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We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In this setting, robots are required to estimate their positions using relative onboard sensing while concurrently tracking the target. Our probabilistic localization and control method takes into account the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. Two measures of uncertainty are extensively evaluated and compared: mutual information and the trace of the extended Kalman filter covariance. Our approach reasons about multiple possible sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations illustrate the performance of our approach and prove its flexibility in finding suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment.

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A decentralized approach for multi-UAV multitarget tracking and surveillance

Cited by 18 publications

References 16 publications

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Cooperative Control for Target Tracking with Onboard Sensing

Cooperative multi-robot control for target tracking with onboard sensing

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