Abstract
The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of A⋆ algorithm, but requires no frontier nodes to be stored in a memory unlike A⋆. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.
Highlights
The cost-effectiveness, ease of access, and mission versatility are the primary compelling qualities of Unmanned Aerial Vehicles (UAVs) that attract many aerospace and related sectors
The challenge in autonomous navigation of a UAV in urban environment is recognizing and localizing obstacles at the right time and continuously adjusting the path of the UAV in such a way that it can avoid the obstacles and navigate to the destination safely. It requires integrating effective object detection and path planning algorithms that run on a companion computer onboard the UAV
Most of the widely used object detection algorithms are based on scanning the entire environment and discretizing the scanned region to create a dense mesh of grid points from which objects are detected
Summary
The cost-effectiveness, ease of access, and mission versatility are the primary compelling qualities of UAVs that attract many aerospace and related sectors. UAVs are being integrated into tasks such as package delivery, first aid, law enforcement, disaster management, infrastructure inspection, agriculture mechanization, rescue, military intelligence, and many more. As low-altitude aerial vehicles, UAVs often encounter obstacles such as trees, mountains, high storey buildings, electric poles, and so on during their missions. These aerial vehicles should be equipped with sensors to perceive the environment around them and avoid potential dangers. To leverage the use of UAVs in cluttered environments, studies have been conducted on the types and ways of integrating various sensors for autonomous navigation. Vehicle localization is one of the pillars of autonomous navigation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
