Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

A Framework for Collaborative Multi-task, Multi-robot Missions

Robotics is a transformative technology that will empower our civilization for a new scale of human endeavors. Massive scale is only possible through the collaboration of individual or groups of robots. Collaboration allows specialization, meaning a multirobot system may accommodate heterogeneous platforms including human partners. This work develops a unified control architecture for collaborative missions comprised of multiple, multi-robot tasks. Using kinematic equations and Jacobian matrices, the system states are transformed into alternative control spaces which are more useful for the designer or more convenient for the operator. The architecture allows multiple tasks to be combined, composing tightly coordinated missions. Using this approach, the designer is able to compensate for non-ideal behavior in the appropriate space using whatever control scheme they choose. This work presents a general design methodology, including analysis techniques for relevant control metrics like stability, responsiveness, and disturbance rejection, which were missing in prior work. Multiple tasks may be combined into a collaborative mission. The unified motion control architecture merges the control space components for each task into a concise federated system to facilitate analysis and implementation. The task coordination function defines task commands as functions of mission commands and state values to create explicit closed-loop collaboration. This work presents analysis techniques to understand the effects of cross-coupling tasks. This work analyzes system stability for the particular control architecture and identifies an explicit condition to ensure stable switching when reallocating robots. We are unaware of any other automated control architectures that address large-scale collaborative systems composed of task-oriented multi-robot coalitions where relative spatial control is critical to mission performance. This architecture and methodology have been validated in experiments and in simulations, repeating earlier work and exploring new scenarios and. It can perform large-scale, complex missions via a rigorous design methodology

Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys

In this thesis is presented a novel approach to model, control, and planning the motion of a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a nonholonomic cart (York mail trolley) in a loosely structured environment. The method is based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its kinematics properties. In consequence, two robot configurations are produced by the task of grasping and ungrasping the load, they are: the single-robot configuration and the robot-trolley configuration. Furthermore, in order to comply with the general planar motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for each configuration, the robot has been provided with two motorized steerable wheels in order to have a flexible platform able to adapt to these restrictions. [Continues.

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized

Multi-Robot Coordination and Scheduling for Deactivation & Decommissioning

Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter. First, we describe a robot equipped with sensors which uses a modified A* path-planning algorithm to navigate in a complex environment with a tether constraint. This is then augmented with an adaptive informative path planning approach that uses the assimilated sensor data within a Gaussian Process distribution model. The model\u27s predictive outputs are used to adaptively plan the robot\u27s path, to quickly map and localize areas from an unknown field of interest. The work was validated in extensive simulation testing and early hardware tests. Next, we focused on how to assign tasks to a heterogeneous set of robots. Task assignment is done in a manner which allows for task-robot dependencies, prioritization of tasks, collision checking, and more realistic travel estimates among other improvements from the state-of-the-art. Simulation testing of this work shows an increase in the number of tasks which are completed ahead of a deadline. Finally, we consider the case where robots are not able to complete planned tasks fully autonomously and require operator assistance during parts of their planned trajectory. We present a sampling-based methodology for allocating operator attention across multiple robots, or across different parts of a more sophisticated robot. This allows few operators to oversee large numbers of robots, allowing for a more scalable robotic infrastructure. This work was tested in simulation for both multi-robot deployment, and high degree-of-freedom robots, and was also tested in multi-robot hardware deployments. The work here can allow robots to carry out complex tasks, autonomously or with operator assistance. Altogether, these three components provide a comprehensive approach towards robotic deployment within the deactivation and decommissioning tasks faced by the Department of Energy