Energy-Aware Trajectory Planning for the Localization of Mobile Devices Using an Unmanned Aerial Vehicle

Artemenko, Oleksandr; Dominic, Omachonu Joshua; Andryeyev, Oleksandr; Mitschele‐Thiel, Andreas

doi:10.1109/icccn.2016.7568517

Cited by 26 publications

(18 citation statements)

References 14 publications

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“…Considering the context of a simple coverage, the most common performance metrics found in the literature are: the total travelled distance or the path length [29,30], the time-to-complete a mission [31,32], the area coverage maximization [33], and the number of turning maneuvers [34,35]. Minimizing the coverage path length lies in a trade-off with the area coverage maximization.…”

Section: Performance Metricsmentioning

confidence: 99%

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

424

207

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Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

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Section: Performance Metricsmentioning

confidence: 99%

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

424

207

View full text Add to dashboard Cite

show abstract

“…Optimizing the trajectory length and the mission completion time is essential in energy-efficient path planning techniuqes for UAVs [7,10,33,34]. The mission completion time includes flying time and hovering time.…”

Section: Completion Timementioning

confidence: 99%

“…Often, the main concerns in CPP are the efficiency and completeness of the coverage. The effectiveness of a CPP work is mainly related to the following performance indices such as the path length [7,8], the mission completion time [9,10], and the number of turns. These metrics are related with energy consumption [11,12].…”

Section: Introductionmentioning

confidence: 99%

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Ghaddar

Merei

2020

Future Internet

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The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

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“…If the ant used the edge (i, j) along the tour, trace amount is calculated according to Eq. (25). Otherwise, the trace amount is zero [74].…”

Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

“…They were originally used for missions tedious or too dangerous for humans and all this due to their ease of deployment, low maintenance cost [21], high mobility and hovering capability [22]. In general, UAVs are preferred for their ability to stabilize at a particular position and altitude [23], to fly at various speeds [24][25][26], to hover in a stationary position over a target [22,27], and to perform all these maneuvers in close proximity to obstacles [28,29].…”

Section: Introductionmentioning

confidence: 99%

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

2020

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Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS's optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller's performance such us ANFIS only and proportional-integral-derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

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Energy-Aware Trajectory Planning for the Localization of Mobile Devices Using an Unmanned Aerial Vehicle

Cited by 26 publications

References 14 publications

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

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