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

Towards an autonomous landing system in presence of uncertain obstacles in indoor environments

The landing task is fundamental to Micro air vehicles (MAVs) when attempting to land in an unpredictable environment (e.g., presence of static obstacles or moving obstacles). The MAV should immediately detect the environment through its sensors and decide its actions for landing. This paper addresses the problem of the autonomous landing approach of a commercial AR. Drone 2.0 in presence of uncertain obstacles in an indoor environment. A localization methodology to estimate the drone's pose based on the sensor fusion techniques which fuses IMU and Poxyz signals is proposed. In addition, a vision-based approach to detect and estimate the velocity, position of the moving obstacle in the drone's working environment is presented. To control the drone landing accurately, a cascade control based on an Accelerated Particle Swarm Optimization algorithm (APSO) is designed. The simulation and experimental results demonstrate that the obtained model is appropriate for the measured data

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

The State-of-Art of Underwater Vehicles - Theories and Applications

An autonomous underwater vehicle (AUV) is an underwater system that contains its own power and is controlled by an onboard computer. Although many names are given to these vehicles, such as remotely operated vehicles (ROVs), unmanned underwater vehicles (UUVs), submersible devices, or remote controlled submarines, to name just a few, the fundamental task for these devices is fairly well defined: The vehicle is able to follow a predefined trajectory. AUVs offer many advantages for performing difficult tasks submerged in water. The main advantage of an AUV is that is does not need a human operator. Therefore it is less expensive than a human operated vehicle and is capable of doing operations that are too dangerous for a person. They operate in conditions and perform task that humans are not able to do efficiently, or at all (Smallwood & Whitcomb, 2004; Horgan & Toal, 2006; Caccia, 2006)

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Development of a Prototype Autonomous Electric Vehicle

The paper presents an Autonomous Electric Vehicle with obstacle avoidance system. This research work made use of ultrasonic sensors, the principle of distance measurements and calculations as well as detecting obstacle on its path. The device consists of three ultrasonic sensors that detect object for each left, right and front of the vehicle, based on developed and installed codes in the Arduino microcontroller and displays the range using ISIS Proteus 8 electronic modelling software. The minimum and maximum range of object detections is 2cm to 400cm respectively. However, the measured distance was from 25cm to 150cm and the corresponding calculated distances using oscilloscope waveforms are 28.10cm and 148.3cm. The difference between the measured and calculated distance was 5.4% on average. GPS navigates the vehicle autonomously to its destination using an algorithm for navigation based on reactive behavior. The vehicle is powered by rechargeable batteries (4 lithium ion batteries) which are charged using external power source by connecting into electricity grid. Furthermore, a solar panel has been utilized as a secondary source of power to charge the batteries. This reduces the dependency of the vehicle on external power sources. The vehicle is capable of moving for about 20m to and fro and avoiding obstacle on its path

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory

Real Time Implementation of Amphibious Unmanned Aerial Vehicle System for Horticulture

Automating the tasks that require manpower has been considered as an area of active research in science and technology. Challenges in designing such systems include accuracy in the parameters of performance, minimal hardware, cost-efficiency, and security. The efficiency of drones designed for replacing humans is often evaluated using their weight, flying time, and power consumption. Herein, the prototype-based Drone model has been designed and discussed for horticulture applications. In this model, a horticulture drone has been designed for structuring and cutting of plants in street interstates. This methodology focuses on automation engineering that is utilized for cutting the plants in less time and less power, thereby diminishing the contamination that may happen by utilizing fuels. The epic part of this plan includes the less weight drone predesigned using Computer-Aided Three-Dimensional Interactive Application (CATIA) V5 Software. The throttle for the motors is adjusted at 50% to get the required thrust for the Unmanned Aerial Vehicle (UAV) to fly. Experimental results show that the horticulture drone has comparatively more flying time and less power consumption.Keywords— CATIA; UAV; Automation; Thrust; Throttle