Towards Robust Autonomous MAV Landing with a Gimbal Camera

As micro aerial vehicles (MAVs) become increasingly common as platforms for aerial inspection, monitoring and tracking, the need for robust automated landing methods increases, for both static and dynamic landing targets. Precision MAV landings are difficult, even for experienced human pilots. While semi-autonomous MAV landings have proven effective, they add additional requirements for multiple skilled operators, which in turn increase the operational costs. This is not always practical and the human in the loop prevents the possibility of more efficient robotic teams that do not require human operators. As such, an automated landing system has been a growing topic of interest to both industry and academia. In this thesis the aim is to address three different issues. First, in order for a MAV to land autonomously onto a moving target, a complete tracking and landing system for MAVs is needed. An end-to-end system termed ATL is introduced. Results show that ATL is able to track and execute a planned trajectory onto a moving landing target at speeds of 10m/s in simulation. Secondly, to enable autonomous MAV landings in GPS-denied environments, multiple cameras are needed for simultaneously tracking the landing target and performing state estimation. With the prevalence of gimbal cameras on commercially available MAVs for applications such as cinematography, it is advantageous to use the gimbal camera along with other cameras on-board for state estimation. An encoder-less gimbal calibration method is introduced to enable gimbal cameras to be used with state estimation algorithms. The method was validated by modifying OKVIS to jointly optimize for the gimbal joint angle. Finally, to achieve full MAV autonomy, all software components on-board must run in real-time on a computer with limited resources. To address this issue and to take advantage of a gimbal camera the Multi-State Constraint Kalman Filter (MSCKF) algorithm is extended by incorporating a gimbal camera. The method was validated in simulation and on a KITTI raw dataset both show promising results

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots