Integration of SLAM and Obstacle Avoidance algorithms

The ability of an unmanned aerial vehicle (UAV) to identify an obstacle in its pat

Fourteen Years of Autonomous Rotorcraft Research at the Georgia Institute of Technology

Presented at the 2nd Asia-Australia Rotorcraft Forum and 4th International Basic Research Conference on Rotorcraft Technology, Tianjin, China, September 8–11, 2013.Copyright ©2013 by the authors, Published with Permission.This paper presents a brief history and description of capabilities of the Georgia Tech Unmanned Aerial Vehicle Research Facility, while extracting and summarizing many significant and applicable results produced in the last fourteen years. Twenty-six selected publications are highlighted, which are representative of the research conducted at GT-UAVRF since 2000. The papers are divided into three groups: 1) development of a fault-tolerant adaptive flight control system, 2) development of vision-based navigation and control algorithms, and 3) special applications. For each group, the research and results are described, with references to the relevant paper(s)

Coordinated Control of Multiple UAVs : Theory and Flight Experiment

This paper proposes a nonlinear control law to realize a coordinated flight of multiple UAVs and evaluates its performance through flight experiments of small fixed-wing UAV platforms. Assuming all-to-all communication, a decentralized coordination control system is designed based on a virtual leader approach. The proposed control design uses a potential function defined on a phase distribution of multi agents. Two advantages of this coordination controller are; i) it can be applied to make different coordination configurations, ii) it is applicable to any number of UAVs, and so it can easily treat an event of addition/deletion of UAV units in a coordination team. The proposed coordination control law is proven to be locally asymptotically stable by using Lyapunov indirect method, and its large domain of attraction is observed in simulation. Furthermore, the controller is implemented onboard ONERA fixed-wing UAV platforms and tested with a mission scenario which includes four different coordination configurations

Coordinated Control of Multiple UAVs : Theory and Flight Experiment

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Sensitivity analysis of a relative navigation solution for unmanned aerial vehicles in a GNSS-denied environment

Cooperative navigation between two or more unmanned aerial vehicles (UAVs) is an important enabling technology for problems such as military reconnaissance, disaster response, and search and rescue. In many of these situations Global Navigation Satellite Systems (GNSS), such as Global Positioning System (GPS), may be unreliable or unavailable due to structural impedance or malicious signal jamming. Therefore, the task of maintaining a reliable relative navigation solution without the use of GNSS is an important need for the aforementioned missions.;To meet this need, this thesis focuses on the relative navigation between two UAVs that are operating in a GNSS-denied environment. In particular, the design and sensitivity of a navigation algorithm are presented. The navigation algorithm presented consists of an Unscented Kalman filter that fuses multiple on-board sensors to estimate the relative pose between two UAVs. These sensors include: strap-down inertial measurement units, ultra-wideband ranging radios, strap-down tri-axial magnetometers, and downward facing cameras. Through the use of a Monte Carlo simulation study, the presented algorithm\u27s performance sensitivity to various sensor payload characteristics, flight dynamics, and initial condition errors is evaluated. Additionally, a research platform that will provide for a future experimental evaluation of the algorithm presented in this thesis has been integrated and tested as part of this work

Rotorigami: A rotary origami protective system for robotic rotorcraft

Applications of aerial robots are progressively expanding into complex urban and natural environments. Despite remarkable advancements in the field, robotic rotorcraft is still drastically limited by the environment in which they operate. Obstacle detection and avoidance systems have functionality limitations and substantially add to the computational complexity of the onboard equipment of flying vehicles. Furthermore, they often cannot identify difficult-to-detect obstacles such as windows and wires. Robustness to physical contact with the environment is essential to mitigate these limitations and continue mission completion. However, many current mechanical impact protection concepts are either not sufficiently effective or too heavy and cumbersome, severely limiting the flight time and the capability of flying in constrained and narrow spaces. Therefore, novel impact protection systems are needed to enable flying robots to navigate in confined or heavily cluttered environments easily, safely, and efficiently while minimizing the performance penalty caused by the protection method. Here, we report the development of a protection system for robotic rotorcraft consisting of a free-to-spin circular protector that is able to decouple impact yawing moments from the vehicle, combined with a cyclic origami impact cushion capable of reducing the peak impact force experienced by the vehicle. Experimental results using a sensor-equipped miniature quadrotor demonstrated the impact resilience effectiveness of the Rotary Origami Protective System (Rotorigami) for a variety of collision scenarios. We anticipate this work to be a starting point for the exploitation of origami structures in the passive or active impact protection of robotic vehicles

Visual Cue based (vehicle to vehicle) cooperative positioning

Formation flight helps multi-agents cooperate visually and accomplish missions effectively. But in order to achieve a good formation shape, the agents forming the swarm must have good inter-communication among them. Currently, the main way of communication between vehicles is done by Radio Frequency, but due to its various drawbacks, the use of RF wants to be limited. Therefore, another way of communication is proposed: the visual cue based communication. In this project, a set of autonomous vehicles will be forced to adopt a certain shape that is related to the received visual cue based marker, that is, a lead vehicle will show a marker to the followers and these will have to perform the shape that is related to the received marker. Therefore, first, a marker detection algorithm is developed which is able of detecting markers. Then, depending on the marker that has been identified, one or another formation will be executed using a potential function based approach. The approach has some visual constraints included in order to adapt it to the real scenario and only relative distances and angles between vehicles will be employed in the potential functions. The whole thesis has been developed in a simulation environment in Matlab and Python. The results show that besides the visual constraints included, the agents are able to position themselves in a formation with equal inter-agent distance

Visual Cue based (vehicle to vehicle) cooperative positioning

Formation flight helps multi-agents cooperate visually and accomplish missions effectively. But in order to achieve a good formation shape, the agents forming the swarm must have good inter-communication among them. Currently, the main way of communication between vehicles is done by Radio Frequency, but due to its various drawbacks, the use of RF wants to be limited. Therefore, another way of communication is proposed: the visual cue based communication. In this project, a set of autonomous vehicles will be forced to adopt a certain shape that is related to the received visual cue based marker, that is, a lead vehicle will show a marker to the followers and these will have to perform the shape that is related to the received marker. Therefore, first, a marker detection algorithm is developed which is able of detecting markers. Then, depending on the marker that has been identified, one or another formation will be executed using a potential function based approach. The approach has some visual constraints included in order to adapt it to the real scenario and only relative distances and angles between vehicles will be employed in the potential functions. The whole thesis has been developed in a simulation environment in Matlab and Python. The results show that besides the visual constraints included, the agents are able to position themselves in a formation with equal inter-agent distance

Image Dependent Relative Formation Navigation for Autonomous Aerial Refueling

This research tests the feasibility, accuracy, and reliability of a predictive rendering and holistic comparison algorithm with use of an optical sensor to provide relative distance and position behind a lead or tanker aircraft. Using an accurate model of a tanker, an algorithm renders image(s) for comparison with actual collected images by a camera installed on the receiver aircraft. Based on this comparison, information used to create the rendered image(s) is used to provide the relative navigation solution required for autonomous air refueling. Given enough predicted images and processing time, this approach should reliably find an accurate solution. Building on previous work, this research aims to minimize the number of required rendered images to provide a real-time navigational solution with sufficient accuracy for an auto-pilot controller installed on future Unmanned Aircraft Systems