An energy-aware drone trajectory planning scheme for terrestrial sensors localization

Kouroshnezhad, Sahar; Peiravi, Ali; Haghighi, Mohammad Sayad; Jolfaei, Alireza

doi:10.1016/j.comcom.2020.02.055

Cited by 16 publications

(3 citation statements)

References 23 publications

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“…"Weighted Energy-aware Trajectory with Adaptive Radius (WETAR)" is a novel drone trajectory planning technique that is presented in [20]. For trajectory planning, the suggested method makes use of linear programming (LP) when sensors obtained throughout a range-free prelocalization stage that have specific estimative regions are present.…”

Section: Related Workmentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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Section: Related Workmentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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“…The fuel sensor system of drone ensures the safety of return back in case of losing the path or anti-attack effects [13]. The most important is to know that the vital advantage of drone control sensor is to reduce the excess on auto pilot regarding to decision making which might lead to flight handle error [14,15]. The combustion engines include 2 types in drone designs, one is external combustion and the other is internal [16,17].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Air-Fuel Percentage Sensors in Drone Engine Controlling

et al. 2022

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This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine. HIGHLIGHTS The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring The obtained results show that the possibility of using engine with different failure mode and fault considerations The represented control structure is simple, efficient and provides the required air-to-fuel ratio

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“…The total energy on a drone is limited thus restricting the flight-time of the application mission. The work in [22] provides an energy-aware approach that uses trajectory planning of drones used as mobile anchors to save energy.…”

Section: Challenges In Drone-based Application Missionsmentioning

confidence: 99%

Location prediction and trajectory optimization in multi-UAV application missions

Singh¹

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Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.

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An energy-aware drone trajectory planning scheme for terrestrial sensors localization

Cited by 16 publications

References 23 publications

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Design and Simulation of Air-Fuel Percentage Sensors in Drone Engine Controlling

Location prediction and trajectory optimization in multi-UAV application missions

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