A hybrid 3D path planning method for UAVs

Ortiz-Arroyo, Daniel

doi:10.1109/red-uas.2015.7440999

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…With the support of state-ofthe-art 3D reconstruction software, the recorded inspection data have been post-processed and dense, high-quality point clouds and triangular meshes have been derived. In [15], a hybrid approach has been employed for near-optimal solution in a 2D environment. A third dimension is managed in an online fashion through a fuzzy controller working simultaneously with a Lazy Theta* algorithm [40].…”

Section: Sampling Based Algorithmsmentioning

confidence: 99%

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Aiello

Valavanis

Rizzo

2022

J Intell Robot Syst

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UAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.

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Section: Sampling Based Algorithmsmentioning

confidence: 99%

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Aiello

Valavanis

Rizzo

2022

J Intell Robot Syst

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“…To enable a UAV to execute a multitude of complex tasks in a 3D space, an approach could be to use lowlevel flight control coupled with a supervisory rule-based or fuzzy controller to optimize the drone's navigation [10]. However, traditional controllers, such as PID, are not effective in handling dynamic changes such as wind gusts or turbulence, which can significantly affect the UAV's flight trajectory [11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reward in DQN for Autonomous Navigation of UAVs Using Object Detection

Lagoda,

Sharifi,

Pedersen

et al. 2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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This paper discusses the implementation of a Deep Reinforcement Learning policy, based on DQN, which optimizes the navigation of the UAV to the front of wind turbine blades. The UAV was trained in simulation using Unreal Engine V4.27 coupled with AirSim. The action space of the UAV was discretized while allowing 6 different actions to be executed. A Yolov5 network trained with images of simulated wind turbines was used for detection and tracking, providing the DQN policy with state information, upon which it has been trained. In addition to this, the dynamic reward has been implemented, which combined both navigation and inspection objectives in the final evaluation of actions. Our tests showed that after 7500 time-steps the exploration rate reached near 0, the mean length of the episodes increased from 10 down to 30, but the mean reward increased from around -60 to stabilizing the output at 26. These results suggest that the proposed method is a promising solution to optimizing the autonomous inspection of wind turbines with UAVs.

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“…To navigate in 3D spaces approaches such as [3] use hybrid techniques using a map of the environment. Other approaches such as [4] focused on semi-autonomous drone inspection and damage detection in images, assuming that the drone was positioned in front of a WT's blade.…”

Section: Introductionmentioning

confidence: 99%

Efficient UAV Autonomous Navigation with CNNs

Mikolaj,

Lauersen,

Tchelet

et al. 2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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This paper presents a novel approach to the navigation of Unmanned Aerial Vehicles (UAV) for the autonomous inspection of wind turbines. Firstly, a Single Shot Detector (SSD) network is trained to detect wind turbines and their subcomponents. Then, an optimized template matching algorithm is used to estimate the distance between the UAV and the wind turbine, using as the template, the SSD bounding box prediction on the left image of a stereo camera. Lastly, an Extended Kalman Filter (EKF) estimates the position of the wind turbine's hub. The EKF is designed to compensate for CNN's latency while sending setpoints to the controller of the UAV.

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A hybrid 3D path planning method for UAVs

Cited by 13 publications

References 16 publications

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Dynamic Reward in DQN for Autonomous Navigation of UAVs Using Object Detection

Efficient UAV Autonomous Navigation with CNNs

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