A Planning Pipeline for Large Multi-Agent Missions

In complex multi-agent applications, human operators are often tasked with planning and managing large heterogeneous teams of humans and autonomous vehicles. Although the use of these autonomous vehicles broadens the scope of meaningful applications, many of their systems remain unintuitive and difficult to master for human operators whose expertise lies in the application domain and not at the platform level. Current research focuses on the development of individual capabilities necessary to plan multi-agent missions of this scope, placing little emphasis on the integration of these components in to a full pipeline. The work presented in this paper presents a complete and user-agnostic planning pipeline for large multiagent missions known as the HOLII GRAILLE. The system takes a holistic approach to mission planning by integrating capabilities in human machine interaction, flight path generation, and validation and verification. Components modules of the pipeline are explored on an individual level, as well as their integration into a whole system. Lastly, implications for future mission planning are discussed

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Advances in Human Robot Interaction for Cloud Robotics applications

In this thesis are analyzed different and innovative techniques for Human Robot Interaction. The focus of this thesis is on the interaction with flying robots. The first part is a preliminary description of the state of the art interactions techniques. Then the first project is Fly4SmartCity, where it is analyzed the interaction between humans (the citizen and the operator) and drones mediated by a cloud robotics platform. Then there is an application of the sliding autonomy paradigm and the analysis of different degrees of autonomy supported by a cloud robotics platform. The last part is dedicated to the most innovative technique for human-drone interaction in the User’s Flying Organizer project (UFO project). This project wants to develop a flying robot able to project information into the environment exploiting concepts of Spatial Augmented Realit

Real-time obstacle collision avoidance for fixed wing aircraft using B-splines

A real-time collision avoidance algorithm is developed based on parameterizing an optimal control problem with B-spline curves. The optimal control problem is formulated in output space rather than control or input space, this is feasible because of the differential flatness of the system for a fixed wing aircraft. The flat output trajectory is parameterized using a Bspline curve representation. In order to reduce the computational time of the optimal problem, the aircraft and obstacle constraints are augmented in the cost function using a penalty function method. The developed algorithm has been simulated and tested in MATLAB/Simulink

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions

A Traffic Control Framework for Uncrewed Aircraft Systems

The exponential growth of Advanced Air Mobility (AAM) services demands assurances of safety in the airspace. This research a Traffic Control Framework (TCF) for developing digital flight rules for Uncrewed Aircraft System (UAS) flying in designated air corridors. The proposed TCF helps model, deploy, and test UAS control, agents, regardless of their hardware configurations. This paper investigates the importance of digital flight rules in preventing collisions in the context of AAM. TCF is introduced as a platform for developing strategies for managing traffic towards enhanced autonomy in the airspace. It allows for assessment and evaluation of autonomous navigation, route planning, obstacle avoidance, and adaptive decision making for UAS. It also allows for the introduction and evaluation of advance technologies Artificial Intelligence (AI) and Machine Learning (ML) in a simulation environment before deploying them in the real world. TCF can be used as a tool for comprehensive UAS traffic analysis, including KPI measurements. It offers flexibility for further testing and deployment laying the foundation for improved airspace safety - a vital aspect of UAS technological advancement. Finally, this papers demonstrates the capabilities of the proposed TCF in managing UAS traffic at intersections and its impact on overall traffic flow in air corridors, noting the bottlenecks and the inverse relationship safety and traffic volume.Comment: 6 pages, 7 figure

TARGET POSE ESTIMATION VIA DEEP LEARNING FOR MILITARY SYSTEMS

Target pose estimation and aimpoint selection is crucial in direct energy weapon systems, as it allows the system to point to a specific and strategic area of the target. However, it is a challenging task because a dedicated attitude sensor is required. Motivated by new emerging deep learning capabilities, the present work proposes a deep learning model to estimate a target spacecraft attitude in terms of Euler angles. Data for the deep learning model were experimentally generated from 3D UAV models, incorporating effects such as atmospheric backgrounds and turbulence. The targets pose was derived from the training, validation, and prediction of 2D keypoints. With a keypoint detection model it is possible to detect interest points in an image, which allows us to estimate pose, angles, and dimensions of the target in question. Utilizing a weak-perspective direct linear transformation algorithm, the pose of a 3D object with respect to a camera from 3D to 2D correspondences could be determined. Additionally, from this correspondence, an aimpoint, mimicking laser tracking could be determined on the target. This work evaluates these methods and their accuracy against experimentally generated data with simulated real-world environments.Outstanding ThesisEnsign, United States NavyApproved for public release. Distribution is unlimited

Advanced Mission Management System for Unmanned Aerial Vehicles

The paper presents advanced mission management system (MMS) for unmanned aerial vehicles, based on integrated modular avionics (IMA) architecture. IMA architecture enables the MMS to host high end functions for autonomous navigation and attack. MMS is a collection of systems to execute the mission objectives. The system constitutes mission computer (MC), sensors and other sub-systems. The MMS-MC needs to execute advanced algorithms like terrain referenced navigation, vision-aided navigation, automatic target recognition, sensor fusion, online path planning, and tactical planning for autonomy and safety. This demands high-end architecture in terms of hardware, software, and communication. The MMS-MC is designed to exploit the benefits of IMA concepts such as open system architecture, hardware and software architecture catering for portability, technology transparency, scalability, system reconfigurability and fault tolerance. This paper investigates on advanced navigation methods for augmenting INS with terrain-referenced navigation and vision-aided navigation during GPS non-availability. This paper also includes approach to implement these methods and simulation results are provided accordingly, and also discusses in a limited way, the approach for implementing online path planning.Defence Science Journal, Vol. 64, No. 5, September 2014, pp.438-444, DOI:http://dx.doi.org/10.14429/dsj.64.599