Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar. For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.Research supported by the Spanish Government through the Cicyt project ADAS ROAD-EYE (TRA2013-48314-C3-1-R)

Obstacle detection technique using multi sensor integration for small unmanned aerial vehicle

Achieving a robust obstacle detection system for small UAV is very challenging. Due to size and weight constraints, very limited detection sensors can be equipped in the system. Prior works focused on a single sensing device which is either camera or range sensors based. However, these sensors have their own advantages and disadvantages in detecting the appearance of the obstacles. In this paper, combination of both sensors based is proposed for a small UAV obstacle detection system. A small Lidar sensor is used as the initial detector and queue for image capturing by the camera. Next, SURF algorithm is applied to find the obstacle sizes estimation by searching the connecting feature points in the image frame. Finally, safe avoidance path for UAV is determined through the exterior feature points from the estimated width of the obstacle. The proposed method was evaluated by conducting experiments in real time with indoor environment. In the experiment conducted, we successfully detect and determine a safe avoidance path for the UAV on 6 different sizes and textures of the obstacles including textureless obstacle

Obstacle Detection for Unmanned Aerial Vehicle (UAV)

This study aims to develop an obstacle detection system for unmanned aerial vehicles utilising the ORB feature extraction. In the past, small unmanned aerial vehicles (UAV) were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses different advantages and disadvantages. As a result, the small unmanned aerial vehicle is unable to determine the obstacle's distance or bearing precisely. Due to physical size restrictions and payload capacity, the lightweight Pi Camera and TF Luna LiDAR sensor were selected as the most suitable sensors for integration. In algorithm development and filtration is used to improve the accuracy of the feature matching process, which is required for classifying the obstacle region and free region of any texture obstacle. The experiment was under the environment of OpenCV and Spyder. In real-time experiment, the success rate for good texture(40%), poor texture (55%) and texture-less (45%) The findings indicate that the recommended method works well for detecting textures-less obstacle even though the success rate is only 40% because out of 10 test only one test is fail on detecting free region . The sensor calibration and constructing convex hull for obstacle detection is recommended in future works to improve the efficiency of the obstacle detection system and classified the free region and obstacle region to create safe avoidance path

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

Achieving a reliable obstacle detection and avoidance system that can provide an effective safe avoidance path for small unmanned aerial vehicle (UAV) is very challenging due to its physical size and weight constraints. Prior works tend to employ the vision based-sensor as the main detection sensor but resulting to high dependency on texture appearance while not having a distance sensing capabilities. The previous system only focused on the detection of the static frontal obstacle without observing the environment which may have moving obstacles. On the other hand, most of the wide spectrum range sensors are heavy and expensive hence not suitable for small UAV. In this work, integration of different based sensors was proposed for a small UAV in detecting unpredictable obstacle appearance situation. The detection of the obstacle is accomplished by analysing the flow field vectors in the image frames sequence. The proposed system was evaluated by conducting the experiments in a real environment which consisted of different configuration of the obstacles. The results from the experiment show that the success rate for detecting unpredictable obstacle appearance is high which is 70% and above. Even though some of the introduced obstacles are considered to have poor texture appearances on their surface, the proposed obstacle detection system was still able to detect the correct appearance movement of the obstacles by detecting the edges

Deployable Hook Retrieval System for UAV Rescue and Delivery

The rapid development of unmanned aerial vehicles (UAVs) has helped expand their practical use to many industrial applications. However, UAVs sometimes suffer from a flight time limitation and/or a loss in communication. Such undesired malfunctions can endanger public safety and incur economic losses. This paper presents a new class of UAV that can retrieve a disabled or malfunctioned UAV from the ground. We developed a deployable hook retrieval system (DHRS) which integrates three principal mechanisms (i.e., deployment, slider-linkage-release, and hook release). Each mechanism plays a role in deploying and retrieving multiple hooks while using a simple control strategy. Through a Finite Element Method simulation, the hook was topologically optimized in order to achieve a high strength while reducing weight. The deployed multiple hooks allow the device to capture the target regardless of its orientation. Due to these design strategies, object recognition using a computer vision was simply demonstrated by exploiting ORB and FLANN algorithms. Through an experimental study, we discussed the target range, success rate, and the practical uses that the DHRS could achieve. The results show that the proposed designs were versatile and consistently successful in capturing the targets while addressing constraints such as power consumption, computational load, and lack of prior knowledge or information about the target

Research on the accuracy of algorithms for autonomous aircraft navigation

Disertacijoje nagrinėjamos nedidelių autonominių orlaivių navigacijos algoritmų įtakos skrydžio tikslumui bei orlaivio deviacijos nuo užduotos skrydžio trajektorijos vertinimo problemos. Pagrindinis tyrimų objektas yra autonominės navigacijos algoritmai. Autonominio skrydžio tikslumas yra tiesiogiai susijęs su skrydžio sauga. Dėl šios priežasties pagrindinis disertacijos tikslas yra ištirti naudojamus ar siūlomus naudoti navigacijos algoritmus bei pateikti autonominio skrydžio saugos gerinimo metodus per navigacijos prizmę. Darbe sprendžiami trys pagrindiniai uždaviniai: autonominio orlaivio navigacijos matematinio modelio parinkimas bei matematinis aprašymas, navigacijos algoritmų įtakos skrydžio tikslumui vertinimas, mažesnę įtaką skrydžio nuokrypiams turinčio algoritmo sukūrimas, kurio paskirtis – saugiai apskristi ir išvengti antžeminių kliūčių. Pirmasis uždavinys skirtas įvertinti didelės imties navigacijos duomenų statistinę aibę. Antrasis bei trečiasis skirti pačių algoritmų analizei. Disertaciją sudaro įvadas, trys skyriai, bendrosios išvados, literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir šeši priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvade pateiktos disertacijos tema autoriaus paskelbtos publikacijos ir pranešimai konferencijose bei disertacijos struktūra. Pirmasis skyrius skirtas literatūros analizei. Jame pateikta autonominių orlaivių klasifikacija. Pateikta autonominių orlaivių navigacijos, kontrolės bei valdymo algoritmų analizė. Skyriaus pabaigoje formuluojamos išvados ir tikslinami disertacijos uždaviniai. Antrajame skyriuje pateiktas tyrimuose taikomas matematinis autonominio orlaivio navigacijos modelis. Pateikta šiuo modeliu gautų navigacijos duomenų analizė bei vertinimas. Trečiajame skyriuje teoriniai rezultatai lyginami su gautais praktinių skrydžių metu bei naudojant SITL (angl. Software In The Loop) skrydžio imitaciją. Pasiūlyta metodika bei autonominių orlaivių navigacijos algoritmas automatizuotam antžeminių kliūčių apskridimui. Disertacijos tema paskelbti 6 straipsniai: du – straipsniai žurnaluose, įtrauktuose į Thomson ISI duomenų bazę, du – recenzuojamuose žurnaluose kitose duomenų bazėse, bei du – kitų tarptautinių bei respublikinių konferencijų medžiagoje. Disertacijos tema perskaityti 6 pranešimai Lietuvos bei kitų šalių konferencijose