IJERT-Vision based Obstacle Avoidance System for Autonomous Aerial Systems (original) (raw)

Obstacle Avoidance System for UAVs using Computer Vision

AIAA Infotech @ Aerospace, 2015

The purpose of this research is to develop an obstacle avoidance system for use on small, fixed-wing Uninhabited Aerial Vehicles (UAVs). In order to detect and avoid obstacles, computer based vision algorithms will be implemented with an automatic flight control system. Images of obstacles are captured using forward facing, externally mounted cameras. Obstacles will include moving and non-moving objects within the flight path of the UAV, which will be detected through the use of optical flow and feature-tracking methods. 1. Motivation and Goals for Research UAVs have the potential to replace inhabited aircraft for many civilian and military applications, which include, but not limited to, disaster relief assistance, search and rescue, and combat zone intelligence gathering. They have lower operating costs and pose minimal risk to human pilots. However,

Collision avoidance for UAV using visual detection

2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011

Unmanned Arial Vehicles (UAVs) require the development of some on-board safety equipments before inheriting the sky. An on-board collision avoidance system is being built by our team. Due to the strict size, weight, power, and costs constraints, visual intruder airplane detection is the only option. This paper introduces our visual airplane detector algorithm, which is designed to be operational in clear and in cloudy situations under regular daylight visual conditions. To be able to implement the algorithm on-board, we have carefully selected topographic operators, which can be efficiently solved on cellular processor arrays.

Obstacle avoidance by unmanned aerial vehicles using image recognition techniques

Unmanned Aerial Vehicles are increasingly being used for military and civilian purposes. Obstacle avoidance is an important aspect for any mobile robot including UAVs. Indoor UAVs traveling through a corridor can autonomously avoid obstacles and do path planning with LIDARs. Outdoor UAVs can detect obstacles using radars. This paper proposes a new algorithm to autonomously avoid obstacles using radars and image processing of video frames to detect and avoid obstacles. Typically, UAVs are limited by on-board computational and memory constraints. This new algorithm aims to reduce the computational requirement. The performance of this algorithm is compared with the brute force pixel-by-pixel comparison or the MLE algorithm.

Robot Vision: Obstacle-Avoidance Techniques for Unmanned Aerial Vehicles

IEEE Robotics & Automation Magazine, 2000

I n this article, a vision-based technique for obstacle avoidance and target identification is combined with haptic feedback to develop a new teleoperated navigation system for underactuated aerial vehicles in unknown environments. A three-dimensional (3-D) map of the surrounding environment is built by matching the keypoints among several images, which are acquired by an onboard camera and stored in a buffer together with the corresponding estimated odometry. Hence, based on the 3-D map, a visual identification algorithm is employed to localize both obstacles and the desired target to build a virtual field accordingly. A bilateral control system has been developed such that an operator can safely navigate in an unknown environment and perceive it by means of a vision-based haptic force-feedback device. Experimental tests in an indoor environment verify the effectiveness of the proposed teleoperated control. Vision-Based Obstacle-Avoidance Techniques Interest in unmanned aerial vehicles (UAVs) has increased due to the wide range of their application fields, which include surveillance, rescue, and inspection. So far, UAVs have mainly been used outdoors with the support of a global positioning system (GPS) for navigation purposes. However, when UAVs are flying indoors in an unknown and unstructured environment, GPS information will not be available. Therefore, in such situations, different navigation and obstacle-avoidance techniques have been investigated using onboard sensors such as lasers, sonars, cameras, radars, and inertial measurement units (IMUs) that give a perception of the environment. Obstacle avoidance is a core issue since any autonomous navigation system must preserve the safety of both the UAV and the surrounding environment. Several approaches can be found to address this problem. In [1] and [2], radar-based navigation and obstacle avoidance are implemented, while a laser range finder for obstacle detection is employed in [3]. The main drawbacks are the high power consumption and weight of these sensors.

Intelligent Autonomy and Vision Based Obstacle Avoidance for Unmanned Air Vehicles

The paper describes the development and implementation of the Visual Threat Awareness (VISTA) system, its integration with the Multi-layer Architecture for Trajectory Replanning and Intelligent plan eXecution (MATRIX) for autonomous intelligent control of Unmanned Aerial Vehicles (UAV), and performance evaluation of the integrated system through flight tests. The VISTA system generates information on the threats and obstacles in real-time, and passes it on to the MATRIX system that makes mission-related decisions and generates new waypoints and a trajectory that safely avoids the obstacle. The VISTA system combines binocular visual stereo, perceptual organization, graph partitioning and feature tracking for a passive system to enable real-time obstacle detection. Computational stereo performance has progressed such that there now exist several commercial or open source implementations that operate at frame rate, but suffer from well known correspondence errors. We show that introducing a global segmentation step after commodity stereo can increase robustness and leverage existing stereo software. The global segmentation step is based on a graph structure appropriate for collision detection, human vision inspired perceptual organization and graph partitioning using the minimum s-t graph cut. This system has been prototyped using Sarnoff Corp's Acadia I vision processor to enable 640x480@10Hz operation on embedded avionics. We describe VISTA system theory and show proof of concept and flight experiment results of the integrated MATRIX/VISTA system on Georgia Tech's GT-Max autonomous helicopter.

A Computer Vision Based Algorithm for Obstacle Avoidance

Information Technology – New Generations, 2018

This paper presents the implementation of an algorithm based on elementary computer vision techniques that allow an UAV (Unmanned Aerial Vehicle) to identify obstacles (including another UAV) and to avoid them, using only a trivial camera and applying six mathematical treatments on image. We applied this algorithm in a drone in real flight.

Development of Autonomous Drones for Adaptive Obstacle Avoidance in Real World Environments

2018

Recently, drones have been involved in several critical tasks such as infrastructure inspection, crisis response, and search and rescue operations. Such drones mostly use sophisticated computer vision techniques to effectively avoid obstacles and, thereby, require high computational power. Therefore, this work tuned and tested a computationally inexpensive algorithm, previously developed by the authors, for adaptive obstacle avoidance control of a drone. The algorithm aims at protecting the drone from entering in complex situations such as deadlocks and corners. The algorithm has been validated through simulation and implemented on a newly developed drone platform for infrastructure inspection. The design of the drone platform and the experimental results are presented in this study.

Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles

2011

Much of the benefits of deploying unmanned aerial vehicles can be derived from autonomous missions. For such missions, however, sense-and-avoid capability (i.e., the ability to detect potential collisions and avoid them) is a critical requirement. Collision avoidance can be broadly classified into global and local path-planning algorithms, both of which need to be addressed in a successful mission. Whereas global path planning (which is mainly done offline) broadly lays out a path that reaches the goal point, local collision-avoidance algorithms, which are usually fast, reactive, and carried out online, ensure safety of the vehicle from unexpected and unforeseen obstacles/collisions. Even though many techniques for both global and local collision avoidance have been proposed in the recent literature, there is a great interest around the globe to solve this important problem comprehensively and efficiently and such techniques are still evolving. This paper presents a brief overview of a few promising and evolving ideas on collision avoidance for unmanned aerial vehicles, with a preferential bias toward local collision avoidance.

IJERT-Design and Fabrication of Obstacle Detection and Warning Unmanned Aerial Vehicle

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/design-and-fabrication-of-obstacle-detection-and-warning-unmanned-aerial-vehicle https://www.ijert.org/research/design-and-fabrication-of-obstacle-detection-and-warning-unmanned-aerial-vehicle-IJERTCONV9IS10012.pdf We are going to demonstrate an innovative and simple solution for obstacle detection and warning system to avoid collision of unnamed aerial vehicle (UAV) optimized for and evaluated with quad copters .The sensors we are going to use are low cost ultrasonic and infrared range finders .There are also more expensive sensors such as laser sensors. This needs to be taken into consideration for the design, implementation, and parameterization of the signal processing and control algorithm for such a system. As a result, a UAV is capable of distancecontrolled collision avoidance, which is more complex and powerful than comparable simple solutions. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance. Some of the major problem in flying a UAV are air safety, insurance and crowed skies which give disadvantages for a UAV to fly in air, it is a simple and innovative solution by designing and fabricating an obstacle detection and warning UAV. while using less power this makes them well suited for long distance missions, such as mapping, surveillance and defense, where long endurance can be an important factor this system can be used in military and civil purpose

A Novel Stereo based Obstacle Avoidance System for Unmanned Aerial Vehicles

International journal of computer applications, 2015

The use of autonomous unmanned aerial vehicle (UAV) has been on the rise. They are used to replace an ever-increasing amount of labor. There is a need for unmanned aerial systems to operate safely in the environment around them. The work in this paper aims at creating an obstacle avoidance system using Stereo Vision. The work uses standard block matching algorithms. OpenCV and the KTTI Vision Benchmark suite is used. The ArduPilot SITL simulator is used for running the algorithms and displaying the results. The droneapi is an application that is used to access the UAV's information and describe new kinds of flight behavior. The application created is known as STOBA (Stereo Based Obstacle Avoidance), which was created to run within the ArduPilot SITL, in order to provide the mentioned obstacle avoidance capability