Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Visual servoing of an autonomous helicopter in urban areas using feature tracking

We present the design and implementation of a vision-based feature tracking system for an autonomous helicopter. Visual sensing is used for estimating the position and velocity of features in the image plane (urban features like windows) in order to generate velocity references for the flight control. These visual-based references are then combined with GPS-positioning references to navigate towards these features and then track them. We present results from experimental flight trials, performed in two UAV systems and under different conditions that show the feasibility and robustness of our approach

Design of an embedded microcomputer based mini quadrotor UAV

This paper describes the design and realization of a mini quadrotor UAV (Unmanned Aerial Vehicle) that has been initiated in the Systems and Control Laboratory at the Computer and Automation Research institute of the Hungarian Academy of Science in collaboration with control departments of the Budapest University of Technology and Economics. The mini quadrotor UAV is intended to use in several areas such as camera-based air-surveillance, traffic control, environmental measurements, etc. The paper focuses upon the embedded microcomputer-based implementation of the mini UAV, describes the elements of the implementation, the tools realized for mathematical model building, as well as obtains a brief outline of the control design

Development of an Emergency Radio Beacon for Small Unmanned Aerial Vehicles

Emergency locator transmitters (ELTs) used to locate manned aircrafts are not well suited to find and recover small crashed unmanned aerial vehicles (UAVs). ELTs utilize an international satellite system for search and rescue (Cospas-Sarsat System), which should leverage its expensive resources to save lives as a priority. Besides, ELTs are too big and heavy to be used within small UAVs. Some of the existing solutions for this problem are based on receivers that detect signal strength, which may be a long and tedious process not suitable for user needs. Others do not have enough range or require radio license and expensive amateur radio receivers. This paper presents an emergency radio beacon specifically designed to locate small UAVs. It is triggered automatically in the event of a crash and allows finding and recovering a crashed UAV in a fast and simple way. It meets not only the required specifications of user-friendliness, size and weight of this kind of application, but also it is a high precision and low cost device. Besides, it has enough range and endurance. The experiments carried out show the operation of the proposed system

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ