Trajectory optimization for mobile access point

Gangula, Rajeev; Kerret, Paul de; Esrafilian, Omid; Gesbert, David

doi:10.1109/acssc.2017.8335587

Cited by 16 publications

(15 citation statements)

References 7 publications

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“…whose diagonal contains the variances of the position and the velocity estimates. The cost function defined in (7) requires knowledge of the actual target position which is the unknown parameter to be estimated, and for this reason (6) is evaluated at the predicted position estimate for time instant k + 1 computed by the ith UAV at time instant k. 4 Here we suppose that the connectivity between nodes is unaltered from time instant k to k + 1, meaning that the ith UAV solves the optimization problem by assuming that, at k + 1, it will communicate with the same neighbors.…”

Section: Problem Statementmentioning

confidence: 99%

“…i |s (k) , defined by the statistical measurement model in (18); • State transition model. It describes how the state evolves in time, in accordance with the dynamic model in (4) and given by f s (k) |s (k−1) . Given this state-space model, an EKF approach can be used because the observation functions in (14) are non-linear and the noises are Gaussian distributed.…”

Section: Uav-target Trackingmentioning

confidence: 99%

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Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

2020

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Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

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Section: Problem Statementmentioning

confidence: 99%

Section: Uav-target Trackingmentioning

confidence: 99%

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

2020

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“…In the case of a simple and known model of the wireless communication rate (assuming circular symmetry and only taking path loss into account), some recent works [13], [7] have optimized the trajectory of the robot. In [6], multiple users of a mobile access point lead to a non-circular, but still known and rather specific shape.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we do provide a numerical model-based solution for the case when the rates are known, and this solution is more general than [13], [7], [6] arbitrarily-shaped rate functions. However, our main interest is different: we focus on the case when the robot does not know the properties of the wireless network in advance, i.e.…”

Section: Introductionmentioning

confidence: 99%

Learning-based control for a communicating mobile robot under unknown rates

Buşoniu

Varma

Morărescu

et al. 2019

2019 American Control Conference (ACC)

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In problems such as surveying or monitoring remote regions, a mobile robot must transmit data over a wireless network with unknown, position-dependent transmission rates. We propose an algorithm to achieve this objective that learns approximations of the rate function and of an optimal-control solution that transmits the data in minimum time. The rates are estimated with supervised learning from the samples observed; and the control is found with dynamic programming sweeps around the current state of the robot that exploit the rate function estimate, combined with online reinforcement learning. For both synthetic and realistic rate functions, our experiments show that the learning algorithm empties the data buffer in less than twice the number of steps achieved by a model-based solution that requires to perfectly know the rate function.

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“…Because UAVs are flexible, portable, inexpensive, and convenient [ 6 ], they have been widely applied for various purposes [ 7 ], i.e., traffic monitoring, forest fire detection emergency event supervision [ 6 , 8 ], data ferrying [ 9 ], communication enhancement [ 10 ], data collection [ 11 ], 3D city map construction [ 12 , 13 ], and wireless connectivity provisioning (e.g., projects Loon by Google and Internet.org by Facebook) [ 14 ]. According to the Federal Aviation Administration, sales of UAVs for commercial purposes are expected to grow from 600,000 in 2016 to 2.7 million by 2020 [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things

Pan

Wen

et al. 2018

Sensors

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With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

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Trajectory optimization for mobile access point

Cited by 16 publications

References 7 publications

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Learning-based control for a communicating mobile robot under unknown rates

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things

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