326 research outputs found
Vision-based Safe Autonomous UAV Docking with Panoramic Sensors
The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked
concerns about safety measures during their missions. To advance towards safer
autonomous aerial robots, this work presents a vision-based solution to
ensuring safe autonomous UAV landings with minimal infrastructure. During
docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper,
we propose the use of a single omnidirectional panoramic camera pointing
upwards from a landing pad to detect and estimate the position of people around
the landing area. The images are processed in real-time in an embedded
computer, which communicates with the onboard computer of approaching UAVs to
transition between landing, hovering or emergency landing states. While
landing, the ground camera also aids in finding an optimal position, which can
be required in case of low-battery or when hovering is no longer possible. We
use a YOLOv7-based object detection model and a XGBooxt model for localizing
nearby people, and the open-source ROS and PX4 frameworks for communication,
interfacing, and control of the UAV. We present both simulation and real-world
indoor experimental results to show the efficiency of our methods
A Depth-Based Computer Vision Approach to Unmanned Aircraft System Landing with Optimal Positioning
High traffic congestion in cities can lead to difficulties in delivering appropriate aid to people in need of emergency services. Developing an autonomous aerial medical evacuation system with the required size to facilitate the need can allow for the mitigation of the constraint. The aerial system must be capable of vertical takeoff and landing to reach highly conjected areas and areas where traditional aircraft cannot access. In general, the most challenging limitation within any proposed solution is the landing sequence. There have been several techniques developed over the years to land aircraft autonomously; however, very little attention has been scoped to operate strictly within highly congested urban-type environments. The goal of this research is to develop a possible solution to achieve autonomous landing based on computer vision-capture systems. For example, by utilizing modern computer vision approaches involving depth estimation through binocular stereo computer vision, a depth map can be developed. If the vision system is mounted to the bottom of an autonomous aerial system, it can represent the area below the aircraft and determine a possible landing zone. In this work, neural networks are used to isolate the ground via the computer vision height map. Then out of the entire visible ground area, a potential landing position can be estimated. An optimization routine is then developed to identify the most optimal landing position within the visible area. The optimization routine identifies the largest identifiable open area near the desired landing location. Web cameras were utilized and processed on a desktop to form a basis for the computer vision system. The algorithms were tested and verified using a simulation effort proving the feasibility of the approach. In addition, the system was tested on a scaled down city scene and was able to determine an optimal landing zone
A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain
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