Effective Real-Time Unmanned Air Vehicle Path Planning in Presence of Threat Netting

Fu, Xiaowei; Gao, Xiaoguang

doi:10.2514/1.i010077

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Among the surveyed papers that took this approach (Kuwata and How 2004;Yu et al 2009;Wan et al 2011;Chen et al 2012;Kothari and Postlethwaite 2013;Q. Wang et al 2014;Xiaowei and Xiaoguang 2014;Yao et al 2015;Zejun et al 2015;Turnbull et al 2016), only Wan et al (2011) and Q. considered both 3D terrain and obstacle avoidance:…”

Section: Smoothing Criterionmentioning

confidence: 99%

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Recent advances in unmanned aerial vehicles real-time trajectory planning

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Labonté

Tarbouchi

et al. 2019

J. Unmanned Veh. Sys.

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The growing interest in unmanned aerial vehicles (UAV) can be attributed to many factors, particularly the potential for them to operate beyond visual line of sight and at increasingly higher levels of autonomy. Trajectory planning defines the extent to which a vehicle is “autonomous”, therefore, to operate at high levels of autonomy, UAV will require real-time embedded trajectory planning that respects a minimum of flyability. This paper presents a review of recent advances in UAV trajectory planning and seeks to clarify the flyability, the real-time, and the embedded aspects, which are not adequately considered by previous survey papers. This work, which specifically focuses on Class II (≥150 and ≤600 kg) and Class III (>600 kg) fixed-wing UAV, analyses 60 papers from the last decade that mention some real-time achievement with respect to UAV trajectory planning. From this analysis, we highlight some challenges and some suggested orientations for future works.

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Section: Smoothing Criterionmentioning

confidence: 99%

“…Wang et al 2014;Z. Wang et al 2014;Xiaowei and Xiaoguang 2014;Zhan et al 2014;Gardi et al 2015a;Ling and Hao 2015;…”

mentioning

confidence: 99%

Recent advances in unmanned aerial vehicles real-time trajectory planning

Allaire

Labonté

Tarbouchi

et al. 2019

J. Unmanned Veh. Sys.

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“…For simplicity, it is assumed that each UAV is equipped with a low-level flight control system [20][21][22][23] that holds constant altitude and speed. A general flight control system-which includes the attitude angles (roll, pitch, and yaw) controller and fast loop attitude angle rates controller-can follow the guidance inputs to generate control surface deflections.…”

Section: Uav Dynamical Modelmentioning

confidence: 99%

Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking

Liu

Gao

2018

Applied Sciences

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In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.Target-motion estimation has been a major problem in the field of target tracking and has received great attention. In target tracking applications, sensors (such as camera, radar, and sonar, etc.) installed on the UAVs can obtain measurements, such as the relative range and azimuth of the target with respect to the position of the airborne UAV. According to the measurement data, the UAV can obtain a target state estimate which generally is suboptimal given the local information. Fortunately, the communication capabilities of the UAVs enable sending their measurement data to a fusion center (e.g., base station), which leads to a global optimal estimate. Most of existing estimation and fusion methods are based on the information filter (IF), which is the information form of the Kalman filter (KF). For the IF, the information vector and matrix are employed instead of the mean and covariance used in the standard KF to represent the Gaussian distribution [2][3][4]. In this way, the IF has advantages to handle sensor fusion tasks and unknown prior covariance conditions. Thus, IF is more widely used than KF in the estimation and fusion problems for multiple sensors [5,6]. Ridley [7] provides a decentralized airborne data fusion method based on the IF for ground targets tracking. Casbeer [8] presented a new information consensus filter (ICF) for distributed dynamic-state estimation, in which estimation is handled by the traditional information filter, while the communication of measurements is handled by a consensus filter. Additionally, the unscented information filter (UIF) has been proposed for distributed estimation with nonlinear dynamics. Lin [9] ...

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Effective 3-D Path Planning for UAV in Presence of Threat Netting

Ling

Hao

2015

2015 Fifth International Conference on Communication Systems and Network Technologies

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Effective Real-Time Unmanned Air Vehicle Path Planning in Presence of Threat Netting

Cited by 5 publications

References 3 publications

Recent advances in unmanned aerial vehicles real-time trajectory planning

Recent advances in unmanned aerial vehicles real-time trajectory planning

Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking

Effective 3-D Path Planning for UAV in Presence of Threat Netting

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