Flight Test Results for UAVs Using Boid Guidance Algorithms

A critical technology for operating groups of Uninhabited Aerial Vehicles (UAVs) is distributed guidance. Distributed guidance allows an operator to command several vehicles at the same time, reduces operator workload, and adds redundancy to the system. Some of the leading software candidates for achieving distributed guidance are known as Boid Guidance Algorithms (BGAs), which are agent-based techniques relying on the interactions of simple behaviors. Flight tests are crucial to the advancement of flight technologies such as BGAs, and this was identified as an important area for development. This paper presents the results from the 2005 flight tests of BGAs at NASA Dryden Flight Research Center with two RnR Products’ APV-3 UAVs employing CloudCap Technology\u27s Piccolo autopilot system. Major challenges in these flight tests include the use of a waypoint-following system, limited computation resources, and management of safety procedures. The conclusions of this work include the need for using a path-following platform and completion of a full system optimization. This work is an important step in the development of a deployable distributed guidance system

Decentralized UAV guidance using modified boid algorithms

Decentralized guidance of Unoccupied Air Vehicles (UAVs) is a very challenging problem. Such technology can lead to improved safety, reduced cost, and improved mission efficiency. Only a few ideas for achieving decentralized guidance exist, the most effective being the boid algorithm. Boid algorithms are rule-based guidance methods derived from observations of animal swarms. In this paper, boid rules are used to autonomously control a group of UAVs in high-level transit simulations. This paper differs from previous work in that, as an alternative to using exponentially scaled behavior weightings, the weightings are computed off-line and scheduled according to a contingency management system. The motivation for this technique is to reduce the amount of on-line computation required by the flight system. Many modifications to the basic boid algorithm are required in order to achieve a flightworthy design. These modifications include the ability to define flight areas, limit turning maneuvers in accordance with the aircraft dynamics, and produce intelligent waypoint paths. The use of a contingency management system is also a major modification to the boid algorithm. A Simple Genetic Algorithm is used to partially optimize the behavior weightings of the boid algorithm. While a full optimization of all contingencies is not performed due to computation requirements, the framework for such a process is developed. Wolfram\u27s Matlab software is used to develop and simulate the boid guidance algorithm. The algorithm is interfaced with Cloud Cap Technology\u27s Piccolo autopilot system for Hardware-in-the-Loop simulations. These high-fidelity simulations prove this technology is both feasible and practical. They also prove the boid guidance system developed herein is suitable for comprehensive flight testing

Study of artificial intelligence and computer vision methods for tracking transmission lines with the AID of UAVs

Currently, Unmanned Aerial Vehicles (UAVs) have been used in the most diverse applications in both the civil and military sectors. In the civil sector, aerial inspection services have been gaining a lot of attention, especially in the case of inspections of high voltage electrical systems transmission lines. This type of inspection involves a helicopter carrying three or more people (technicians, pilot, etc.) flying over the transmission line along its entire length which is a dangerous service especially due to the proximity of the transmission line and possible environmental conditions (wind gusts, for example). In this context, the use of UAVs has shown considerable interest due to their low cost and safety for transmission line inspection technicians. This work presents research results related to the application of UAVs for transmission lines inspection, autonomously, allowing the identification of invasions of the transmission line area as well as possible defects in components (cables, insulators, connection, etc.) through the use of Convolutional Neural Networks (CNN) for fault detection and identification. This thesis proposes the development of an autonomous system to track power transmission lines using UAVs efficiently and with low implementation and operation costs, based exclusively on rea-time image processing that identifies the structure of the towers and transmission lines durin the flight and controls the aircraft´s movements, guiding it along the closest possible path. A sumary of the work developed will be presented in the next sections.Atualmente, os Veículos Aéreos Não Tripulados – VANTs têm sido utilizados nas mais diversas aplicações tanto no setor civil quanto militar. No setor civil, os serviços de inspeção aérea vêm ganhando bastante atenção, principalmente no caso de inspeções de linhas de transmissão de sistemas elétricos de alta tensão. Este tipo de inspeção envolve um helicóptero transportando três ou mais pessoas (técnicos, pilotos, etc.) sobrevoando a linha de transmissão em toda a sua extensão, o que constitui um serviço perigoso principalmente pela proximidade da linha de transmissão e possíveis condições ambientais (rajadas de vento, por exemplo). Neste contexto, a utilização de VANTs tem demonstrado considerável interesse devido ao seu baixo custo e segurança para técnicos de inspeção de linhas de transmissão. Este trabalho apresenta resultados de pesquisas relacionadas à aplicação de VANTs para inspeção de linhas de transmissão, de forma autônoma, permitindo a identificação de invasões da área da linha de transmissão bem como possíveis defeitos em componentes (cabos, isoladores, conexões, etc.) através do uso de Convolucional. Redes Neurais - CNN para detecção e identificação de falhas. Esta tese propõe o desenvolvimento de um sistema autônomo para rastreamento de linhas de transmissão de energia utilizando VANTs de forma eficiente e com baixos custos de implantação e operação, baseado exclusivamente no processamento de imagens em tempo real que identifica a estrutura das torres e linhas de transmissão durante o voo e controla a velocidade da aeronave. movimentos, guiando-o pelo caminho mais próximo possível. Um resumo do trabalho desenvolvido será apresentado nas próximas seções

Functionality, Complexity, and Approaches to Assessment of Resilience Under Constrained Energy and Information

While system functions, functionality, and complexity are widely used concepts in systems engineering, there is significant diversity in their definitions and no unified approach to measurement. This research establishes a method for measuring impacts to functionality in dynamic engineered systems based on changes in kinetic energy. This metric is applied at particular levels of abstraction and system scales, consistent with the established multiscale nature of systems. By measuring system behavior in context with expected scenarios, it is possible to estimate expected functionality or set bounds on a system\u27s maximum functionality. Functionality and system effectiveness is heavily influenced by the amount of available energy and the information a system has about its environment. A framework is needed for quickly assessing the impact of changes in information in order to drive system architecture and design. This research relates functionality to the information content required to describe a system using principles from information theory and complexity theory

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

Multi-Agent Based Simulation of an Unmanned Aerial Vehicles System

The rapid growth of using Unmanned Aerial Vehicles (UAV) for civilian and military applications has promoted the development of research in many areas. Most of the unmanned aerial vehicles in use are manually controlled. Often, UAVs require highly trained pilot operators. Hence, the main challenge faced by researchers has been to make UAVs autonomous or semiautonomous. The goal of this research project is to develop and implement a simulation for a user-defined environment allowing UAVs to maneuver in free environments and obstacle-laden environments using Boid's algorithm of flocking with obstacle avoidance. The users are permitted to analyze the maneuvering area and coverage efficiency of the UAVs and to dynamically change environments. This project makes use of Boid's flocking algorithm to generate different kinds of movements for the flying agents, enabling the user to analyze the effectiveness of patrolling in that particular scenario. The number of UAVs and the type of environment are set by the user. The set number of UAVs moves as a flock or swarm inside the set environment by using Boid's rules of flocking: cohesion, alignment, and separation. The coverage efficiency of the UAVs in that particular environment is reported based on the ratio between the area covered and the time when the search time reaches a threshold. The advantages and feasibilities of the approach are discussed with the simulation results

2004 Research Engineering Annual Report

Selected research and technology activities at Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts

Realistic Physical Simulation and Analysis of Shepherding Algorithms using Unmanned Aerial Vehicles

Advancements in UAV technology have offered promising avenues for wildlife management, specifically in the herding of wild animals. However, existing algorithms frequently simulate two-dimensional scenarios with the unrealistic assumption of continuous knowledge of animal positions or involve the use of a scouting UAV in addition to the herding UAV to localize the position of the animals. Addressing this shortcoming, our research introduces a novel herding strategy using a single UAV, integrating a computer vision algorithm in a three-dimensional simulation through the Gazebo platform with Robot Operating System 2 (ROS2) middleware. The UAV, simulated with a PX4 flight controller, detects animals using ArUco markers and uses their real-time positions to update their last known positions. The performance of our computer-vision-assisted herding algorithm was evaluated in comparison with conventional, position-aware/dual UAV herding strategies. Findings suggest that one of our vision-based strategies exhibits comparable performance to the baseline for smaller populations and loosely packed scenarios, albeit with sporadic herding failures and performance decrement in very tightly packed flocking scenarios and very sparsely distributed flocking scenarios. The proposed algorithm demonstrates potential for future real-world applications, marking a significant stride towards realistic, autonomous wildlife management using UAVs in three-dimensional spaces