Embedded Vision System for Automated Drone Landing Site Detection

Fraczek, Patryk; Mora, André; Kryjak, Tomasz

doi:10.1007/978-3-030-00692-1_35

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…An unmanned aerial vehicle landing system with autonomous monocular vision for usage in emergencies and unstructured conditions. A unique map representation strategy that builds a grid map with different heights using three-dimensional characteristics collected from Simultaneous Localization and Mapping (SLAM) [50].…”

Section: Landing On a Known Areamentioning

confidence: 99%

Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles

Ghous,

Malik,

Majeed

et al. 2023

VAWKUM trans. comput. sci.

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Nowadays, aerial vehicles (drones) are becoming more popular. Over the past few years, Unmanned Aerial Vehicles (UAVs) have been used in various remote sensing applications. Every aerial vehicle is now either partially or completely automated. The tiniest type of aerial vehicle is the UAV. The widespread use of aerial drones requires numerous safe landing site detection techniques. The paper aims to review literature on techniques for automatic safe landing of aerial drone vehicles by detecting suitable landing sites, considering factors such as ground surfaces and using image processing methods. A drone must determine whether the landing zones are safe for automatic landing. Onboard visual sensors provide potential information on outdoor and indoor ground surfaces through signals or images. The optimal landing locations are then determined from the input data using various image processing and safe landing area detection (SLAD) methods. UAVs are acquisition systems that are quick, efficient, and adaptable. We discuss existing safe landing detection approaches and their achievements. Furthermore, we focus on possible areas for improvement, strength, and future approaches for safe landing site detection. The research addresses the increasing need for safe landing site detection techniques in the widespread use of aerial drones, allowing for automated and secure landing operations.

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Section: Landing On a Known Areamentioning

confidence: 99%

Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles

Ghous,

Malik,

Majeed

et al. 2023

VAWKUM trans. comput. sci.

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“…These solutions are more flexible and allow robots to land safely in unstructured and unknown environments [8]. To identify a suitable spot without maps known a priori, they employ RGB, depth and semantic images, and output the desired landing location [7], [14]. They are able to actively avoid collisions against obstacles by first generating a map of the area online, and then planning in it [8].…”

Section: Related Workmentioning

confidence: 99%

“…Another line of research utilizes Neural Networks (NNs) for binary classification of the terrain into either safe or hazardous areas [7], while performing landing site detection and tracking of people [5]. The training is performed on either satellite images or synthetic datasets [16], with very few works tested in real-world experiments [14]. Furthermore, this category of approaches performs binary classification of the landing area, not exploiting the complete semantic knowledge extracted from sensor readings.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Emergency Landing for Multicopters using Deep Reinforcement Learning

Bartolomei

Kompis

Teixeira

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This work presents a pipeline for autonomous emergency landing for multicopters, such as rotary wing Unmanned Aerial Vehicles (UAVs), using deep Reinforcement Learning (RL). Mechanical malfunctions, strong winds, sudden battery life drops (e.g. due to cold weather), failure in localization or GPS jamming are not uncommon and all constitute emergency situations that require a UAV to abort its mission early and land as quickly as possible in the immediate vicinity. To this end, it is crucial for a UAV that is deployed in real missions to be able to detect a safe landing spot efficiently and proceed to land autonomously, avoiding damage to both its integrity and the surroundings. Driven by the advances in semantic segmentation and depth completion using machine learning, the proposed architecture uses deep RL to infer actions from semantic and depth information, flying the robot towards secure areas, while respecting safety constraints. Thanks to our robust training strategy and the choice of these mid-level representations as input to the RL agent, we show that our policy can directly transfer to the real world, without the need for any additional fine-tuning. In a series of challenging experiments both in simulation and with a real platform, we demonstrate that our planner guides a rotorcraft UAV to a safe landing spot up to 1.5 times faster and with double success rate than the state of the art (including a commercially available solution), paving the way towards realistically deployable UAVs.

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“…Accordingly, acquiring an embedded vision system for landing site detection can be one solution to address this obstacle. These systems normally consist of a vision marker and sensor along with an embedded system which holds a deep learning algorithm, such as a Convolutional Neural Network to detect and position the marker [38], [39].…”

Section: Limitationsmentioning

confidence: 99%

A Data-Driven Simulator for the Strategic Positioning of Aerial Ambulance Drones Reaching Out-of-Hospital Cardiac Arrests: A Genetic Algorithmic Approach

Mackle

Bond

Torney

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Objective: The Internet of Things provide solutions for many societal challenges including the use of unmanned aerial vehicles to assist in emergency situations that are out of immediate reach for traditional emergency services. Out of hospital cardiac arrest (OHCA) can result in death with less than 50% of victims receiving the necessary emergency care on time. The aim of this study is to link real world heterogenous datasets to build a system to determine the difference in emergency response times when having aerial ambulance drones available compared to response times when depending solely on traditional ambulance services and lay rescuers who would use nearby publicly accessible defibrillators to treat OHCA victims. Method: The system uses the geolocations of public accessible defibrillators and ambulance services along with the times when people are likely to have a cardiac arrest to calculate response times. For comparison, a Genetic Algorithm has been developed to determine the strategic number and positions of drone bases to optimize OHCA emergency response times. Conclusion: Implementation of a nationwide aerial drone network may see significant improvements in overall emergency response times for OHCA incidents. However, the expense of implementation must be considered.

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Embedded Vision System for Automated Drone Landing Site Detection

Cited by 7 publications

References 11 publications

Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles

Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles

Autonomous Emergency Landing for Multicopters using Deep Reinforcement Learning

A Data-Driven Simulator for the Strategic Positioning of Aerial Ambulance Drones Reaching Out-of-Hospital Cardiac Arrests: A Genetic Algorithmic Approach

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