Field Tests with an Aerial-Ground Convoy System for Collaborative Tasks

This chapter presents the design, implementation and field experiments of a convoy between an aerial and a terrestrial robot. The convoy strategy proposed is indeed very simple and based in a PD control law. We introduce the robots Pinky and Gaia, robots which have been part of the FRACTAL fleet, the general system set up is also addressed, such as the ground station workloads and the middleware architecture. Finally, comprehensive experimental results shown herein, demonstrate the good performance and usability of the system in multi-robot behavioral research

NAVIGATION AND AUTONOMOUS CONTROL OF MAVS IN GPS-DENIED ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Study and development of a reliable fiducials-based localization system for multicopter UAVs flying indoor

openThe recent evolution of technology in automation, agriculture, IoT, and aerospace fields has created a growing demand for mobile robots capable of autonomous operation and movement to accomplish various tasks. Aerial platforms are expected to play a central role in the future due to their versatility and swift intervention capabilities. However, the effective utilization of these platforms faces a significant challenge due to localization, which is a vital aspect for their interaction with the surrounding environment. While GNSS localization systems have established themselves as reliable solutions for open-space scenarios, the same approach is not viable for indoor settings, where localization remains an open problem as it is witnessed by the lack of extensive literature on the topic. In this thesis, we address this challenge by proposing a dependable solution for small multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based localization system reconstructs the pose by fusing data from onboard sensors. The primary source of information stems from the recognition of AprilTags fiducial markers, strategically placed in known positions to form a “map”. Building upon prior research and thesis work conducted at our university, we extend and enhance this system. We begin with a concise introduction, followed by a justification of our chosen strategies based on the current state of the art. We provide an overview of the key theoretical, mathematical, and technical aspects that support our work. These concepts are fundamental to the design of innovative strategies that address challenges such as data fusion from different AprilTag recognition and the elimination of misleading measurements. To validate our algorithms and their implementation, we conduct experimental tests using two distinct platforms by using localization accuracy and computational complexity as performance indices to demonstrate the practical viability of our proposed system. By tackling the critical issue of indoor localization for aerial platforms, this thesis tries to give some contribution to the advancement of robotics technology, opening avenues for enhanced autonomy and efficiency across various domains.The recent evolution of technology in automation, agriculture, IoT, and aerospace fields has created a growing demand for mobile robots capable of autonomous operation and movement to accomplish various tasks. Aerial platforms are expected to play a central role in the future due to their versatility and swift intervention capabilities. However, the effective utilization of these platforms faces a significant challenge due to localization, which is a vital aspect for their interaction with the surrounding environment. While GNSS localization systems have established themselves as reliable solutions for open-space scenarios, the same approach is not viable for indoor settings, where localization remains an open problem as it is witnessed by the lack of extensive literature on the topic. In this thesis, we address this challenge by proposing a dependable solution for small multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based localization system reconstructs the pose by fusing data from onboard sensors. The primary source of information stems from the recognition of AprilTags fiducial markers, strategically placed in known positions to form a “map”. Building upon prior research and thesis work conducted at our university, we extend and enhance this system. We begin with a concise introduction, followed by a justification of our chosen strategies based on the current state of the art. We provide an overview of the key theoretical, mathematical, and technical aspects that support our work. These concepts are fundamental to the design of innovative strategies that address challenges such as data fusion from different AprilTag recognition and the elimination of misleading measurements. To validate our algorithms and their implementation, we conduct experimental tests using two distinct platforms by using localization accuracy and computational complexity as performance indices to demonstrate the practical viability of our proposed system. By tackling the critical issue of indoor localization for aerial platforms, this thesis tries to give some contribution to the advancement of robotics technology, opening avenues for enhanced autonomy and efficiency across various domains

Collision-free Navigation System for Robotic Helicopter

Tato práce je zaměřena na vytvoření bezkolízního navigačního systému pro robotickou helikopteru. Během této práce je odvozen a linearizován matematický model kvadrakoptéry. Regulátor pro UAV je navržen na základě tohoto modelu. Řešení problému s lokalizací je poskytnuto ve formě Kalmanova filtru. Pro uložení konfiguračního prostoru robota bude navržena prostorově efektivní struktura octree a pro navigaci v tomto prostředí je použit algoritmus A*. Implementace navržených algoritmů je provedena v programovacím jazyce C++ a testována v simulačním prostředí Webots.This work is focused on the creation of a collision-free navigation system for a robotic helicopter. During this work the matematical model of the quadracopter is derived and linearized. The regulator for the UAV is designed based on this model. The solution for the localization problem is provided in the form of Kalman filter. Space-efficient octree structure is proposed to store robot configuration space and A* algorithm is used for navigation in this environment. The implementation of the proposed algorithms is done in programming language C++ and tested in simulation environment Webots