Safe Trajectory Planning for Multiple Aerial Vehicles with Segmentation-Adaptive Pseudospectral Collocation

IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015 Seattle, WA, USAThis paper proposes a method called Segmentation-adaptive Pseudospectral collocation to address the problem of safe trajectory generation in missions with cooperating multiple aerial vehicles. Pseudospectral collocation can generate optimized collision-free trajectories, but for multiple aerial vehicles it cannot guarantee that the safety separation distance is maintained in the whole trajectories, since the constraints are only enforced in discrete points in the trajectory (collocation points). Hp-adaptive pseudospectral collocation increases iteratively the number of collocation points and the degree of the approximating polynomial, but this may lead to an exponential increase of the computational load. The proposed method solves the problem by selectively adding new collocation points where they are needed, only in the segments with conflicts in each iteration, thus effectively reducing the number of collocation points and the computation time with respect to other pseudospectral collocation formulations. The proposed method allows both changes of speed and changes of heading for each aerial vehicle to guarantee the safety distance between them. Its computational load and scalability are studied in randomly generated scenarios. Moreover, a comparison with other method is presented. Several experiments to test the validity of the approach have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies.Comisión europea FP7 ICT (288082)Junta de Andalucía P11-TIC-706

Trajectory planning based on collocation methods for multiple aerial and ground autonomous vehicles

Esta tesis doctorar presenta una serie de contribuciones en los métodos de coordinación y generación de trayectorias de grupos de vehículos, concretamente de vehículos autónomos. Los métodos de colocación, más conocidos por su nombre en inglés “Collocation methods”, han despertado un creciente interés en los últimos años, entre los distintos métodos numéricos para resolver cualquier tipo de problema dentro del campo de la ingeniería. Esta tesis en concreto, presenta un nuevo punto de vista dentro de los métodos de generación de trayectorias, gracias al uso de los métodos de colocación. El interés sobre los vehículos autónomos se ha visto intensificado en los últimos años. Gracias a la evolución de los sensores, la obtención de información del medio que rodea a un vehículo es cada vez más sencilla y fiable. Esto permite a los sistemas de navegación de los vehículos generar cada vez mejores trayectorias libres de colisiones. Esta habilidad también permite a los vehículos autónomos planificar rutas óptimas, evitar obstáculos, seguir algún objetivo, o muchas otras tareas. Inicialmente, el interés sobre los vehículos autónomos recaía principalmente en las aplicaciones militares, especialmente en los vehículos aéreos, conocidos como UAVs o “Drones”. Pero con el paso del tiempo, las aplicaciones civiles o domésticas están sobre pasando los intereses militares. Estas aplicaciones incluyen tanto a vehículos terrestres como aéreos, aunque el impacto sobre los vehículos autónomos aéreos (UAVs) es mucho mayor. Esto es debido a que la accesibilidad y maniobrabilidad de estos vehículos ofrece más ventajas que los vehículos autónomos terrestres (UGVs) en aplicaciones como localización, seguimiento, adquisición de imágenes, generación de mapas, etc. Esta tesis doctoral presenta un nuevo método centralizado para la generación de trayectorias para múltiples vehículos autónomos. Este método se puede usar tanto para vehículos terrestres como aéreos, e incluso en escenarios mixtos con ambos tipos de vehículos. Dicho método está basado en los métodos de colocación Pseudoespectrales, más conocido en inglés como “Pseudospectral (PS) collocation methods”. Estos métodos son muy utilizados para resolver problemas de control óptimos, y se caracterizar porque resuelven dicho problema numéricamente. En el caso de generación de trayectorias, el problema es formulado como un problema de control óptimo, incluyendo las ecuaciones diferenciales que definen la dinámica de los vehículos, las propias restricciones físicas de los actuadores del vehículo, así como las dimensiones del escenario y restricciones de distancia de seguridad entre los distintos vehículos. Luego, se define una función de costes que debe de ser optimizada, como por ejemplo, la distancia de navegación o el propio consumo del vehículo. Los métodos de colocación Pseudospectrales tratan de resolver el problema de optimización aproximando el vector de estado y de control por una serie de polinomios en una serie de puntos denominados puntos de colocación o “collocation points” en inglés. Las restricciones dinámicas de movimiento y las restricciones del problema también deben de cumplirse en dichos puntos. De esta manera, cuando el problema está discretizado y parametrizado, se produce una transformación al paradigma algebraico. Todo el problema se transforma en un problema de Programación no lineal (PNL), el cual será resuelto por algún programa de optimización como por ejemplo puede ser el “SNOPT solver”. Esta forma concreta de modelado del problema de generación de trayectorias permite obtener trayectorias mucho más realistas que son a su vez, más fácil de seguir por el vehículo en cuestión. Esta tesis presenta también un profundo estudio del comportamiento de los distintos métodos de colocación cuando son usados como generadores de trayectorias. A lo largo de la tesis se ha visto que aspectos como la discretización o la aproximación polinómica afectan a la solución del problema, y se ha analizado cómo afecta a otros aspectos como la integridad del sistema, escalabilidad del método (como influye el incremento de vehículos considerados en la planificación), tiempo de computo necesario para obtener una solución, etc. Un resumen de los objetivos que se han abarcado durante el desarrollo de la tesis se presenta a continuación: • Clasificación exhaustiva de los distintos métodos de colocación. Este punto intenta hacer una distinción entre clásicos métodos de colocación Directos y los nuevos Pseudoespectrales. Presentando una descripción completa de estos últimos. • Análisis de los métodos de colocación en problemas de generación de trayectorias. Los métodos de colocación son métodos de propósito general, de manera que se pretende analizar las ventajas y desventajas de estos métodos en los problemas de generación de trayectorias. • Estudio de rendimiento de los métodos de colocación. Aspectos como la calidad de las soluciones obtenidas, escalabilidad, tiempo de cómputo para obtener una solución, aplicaciones de tiempo real, etc. son estudiados en los distintos métodos. • Búsqueda de configuraciones que mejoren el rendimiento. En este apartado se pretende sintonizar los parámetros de configuración de algunos métodos de colocación para buscar un óptimo rendimiento. • Desarrollo de un nuevo algoritmo denominado método de colocación S-Adaptive. Este es un algoritmo desarrollado específicamente para la generación de trayectorias. Este método resuelve toda las desventajas que se producen en los métodos de colocación clásicos. • Desarrollo de escenarios con vehículos terrestres en presencia de obstáculos. Los métodos de colocación han sido muy utilizados en aplicaciones aeronáuticas. Un claro ejemple de ello es la gran cantidad de artículos que se pueden encontrar en la literatura. Es por esto que el uso de vehículos terrestres y concretamente, su uso en presencia de múltiple obstáculos fijos en dichos escenarios, supone una novedad en sí. • Validación experimental de los algoritmos. Este punto se centra en la validación de los resultados obtenidos en las fases de desarrollo y simulación, con vehículos reales. Una gran cantidad de escenarios son presentados con vehículos autónomos, tanto terrestres como aéreos. Todos estos experimentos están dentro del marco de desarrollo del proyecto europeo de investigación EC-SAFEMOBIL “Estimation and Control for SAFE wireless high MOBILity cooperative industrial systems”

LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid

The demand for reliable obstacle warning and avoidance capabilities to ensure safe low-level flight operations has led to the development of various practical systems suitable for fixed and rotary wing aircraft. State-of-the-art Light Detection and Ranging (LIDAR) technology employing eye-safe laser sources, advanced electro-optics and mechanical beam-steering components delivers the highest angular resolution and accuracy performances in a wide range of operational conditions. LIDAR Obstacle Warning and Avoidance System (LOWAS) is thus becoming a mature technology with several potential applications to manned and unmanned aircraft. This paper addresses specifically its employment in Unmanned Aircraft Systems (UAS) Sense-and-Avoid (SAA). Small-to-medium size Unmanned Aerial Vehicles (UAVs) are particularly targeted since they are very frequently operated in proximity of the ground and the possibility of a collision is further aggravated by the very limited see-and-avoid capabilities of the remote pilot. After a brief description of the system architecture, mathematical models and algorithms for avoidance trajectory generation are provided. Key aspects of the Human Machine Interface and Interaction (HMI2) design for the UAS obstacle avoidance system are also addressed. Additionally, a comprehensive simulation case study of the avoidance trajectory generation algorithms is presented. It is concluded that LOWAS obstacle detection and trajectory optimisation algorithms can ensure a safe avoidance of all classes of obstacles (i.e., wire, extended and point objects) in a wide range of weather and geometric conditions, providing a pathway for possible integration of this technology into future UAS SAA architectures

Stochastic Real-time Optimal Control: A Pseudospectral Approach for Bearing-Only Trajectory Optimization

A method is presented to couple and solve the optimal control and the optimal estimation problems simultaneously, allowing systems with bearing-only sensors to maneuver to obtain observability for relative navigation without unnecessarily detracting from a primary mission. A fundamentally new approach to trajectory optimization and the dual control problem is developed, constraining polynomial approximations of the Fisher Information Matrix to provide an information gradient and allow prescription of the level of future estimation certainty required for mission accomplishment. Disturbances, modeling deficiencies, and corrupted measurements are addressed with recursive updating of the target estimate with an Unscented Kalman Filter and the optimal path with Radau pseudospectral collocation methods and sequential quadratic programming. The basic real-time optimal control (RTOC) structure is investigated, specifically addressing limitations of current techniques in this area that lose error integration. The resulting guidance method can be applied to any bearing-only system, such as submarines using passive sonar, anti-radiation missiles, or small UAVs seeking to land on power lines for energy harvesting. Methods and tools required for implementation are developed, including variable calculation timing and tip-tail blending for potential discontinuities. Validation is accomplished with simulation and flight test, autonomously landing a quadrotor helicopter on a wire

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

The post-911 environment has punctuated the force-multiplying capabilities that Remotely Piloted Aircraft (RPA) provides combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to provide for the safe integration of civil unmanned aircraft systems into the national airspace system (NAS) as soon as practicable, but not later than September 30, 2015. However, a necessary entrance criterion to operate RPAs in the NAS is the ability to Sense and Avoid (SAA) both cooperative and noncooperative air traffic to attain a target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, develop techniques for calculating optimal avoidance trajectories, and second, develop techniques for estimating an intruder aircraft\u27s trajectory in a stochastic environment. This dissertation describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for different collision avoidance scenarios

Simplex Control Methods for Robust Convergence of Small Unmanned Aircraft Flight Trajectories in the Constrained Urban Environment

Constrained optimal control problems for Small Unmanned Aircraft Systems (SUAS) have long suffered from excessive computation times caused by a combination of constraint modeling techniques, the quality of the initial path solution provided to the optimal control solver, and improperly defining the bounds on system state variables, ultimately preventing implementation into real-time, on-board systems. In this research, a new hybrid approach is examined for real-time path planning of SUAS. During autonomous flight, a SUAS is tasked to traverse from one target region to a second target region while avoiding hard constraints consisting of building structures of an urban environment. Feasible path solutions are determined through highly constrained spaces, investigating narrow corridors, visiting multiple waypoints, and minimizing incursions to keep-out regions. These issues are addressed herein with a new approach by triangulating the search space in two-dimensions, or using a tetrahedron discretization in three-dimensions to define a polygonal search corridor free of constraints while alleviating the dependency of problem specific parameters by translating the problem to barycentric coordinates. Within this connected simplex construct, trajectories are solved using direct orthogonal collocation methods while leveraging navigation mesh techniques developed for fast geometric path planning solutions. To illustrate two-dimensional flight trajectories, sample results are applied to flight through downtown Chicago at an altitude of 600 feet above ground level. The three-dimensional problem is examined for feasibility by applying the methodology to a small scale problem. Computation and objective times are reported to illustrate the design implications for real-time optimal control systems, with results showing 86% reduction in computation time over traditional methods

Next generation flight management systems for manned and unmanned aircraft operations - automated separation assurance and collision avoidance functionalities

The demand for improved safety, efficiency and dynamic demand-capacity balancing due to the rapid growth of the aviation sector and the increasing proliferation of Unmanned Aircraft Systems (UAS) in different classes of airspace pose significant challenges to avionics system developers. The design of Next Generation Flight Management Systems (NG-FMS) for manned and unmanned aircraft operations is performed by addressing the challenges identified by various Air Traffic Management (ATM) modernisation programmes and UAS Traffic Management (UTM) system initiatives. In particular, this research focusses on introducing automated Separation Assurance and Collision Avoidance (SA&CA) functionalities (mathematical models) in the NG-FMS. The innovative NG-FMS is also capable of supporting automated negotiation and validation of 4-Dimensional Trajectory (4DT) intents in coordination with novel ground-based Next Generation Air Traffic Management (NG-ATM) systems. One of the key research contributions is the development of a unified method for cooperative and non-cooperative SA&CA, addressing the technical and regulatory challenges of manned and unmanned aircraft coexistence in all classes of airspace. Analytical models are presented and validated to compute the overall avoidance volume in the airspace surrounding a tracked object, supporting automated SA&CA functionalities. The scientific basis of this approach is to assess real-time measurements and associated uncertainties affecting navigation states (of the host aircraft platform), tracking observables (of the static or moving object) and platform dynamics, and translate them to unified range and bearing uncertainty descriptors. The SA&CA unified approach provides an innovative analytical framework to generate high-fidelity dynamic geo-fences suitable for integration in the NG-FMS and in the ATM/UTM/defence decision support tools

A framework for modeling and simulation of control, navigation, and surveillance for unmanned aircraft separation assurance

The integration of Unmanned Aircraft Systems in the National Airspace System (UASNAS) problem has received much attention because of the growing number of variety of mission types and the rapid growth of UAS market. Among the many challenging UASNAS problems, separation assurance is considered to be particularly complex, having many interactions among the elements in different levels of abstraction and coupling effects between the different disciplinary domains. In order to explore the separation assurance problem, an analytic model should capture diverse operational scenarios, vehicle dynamics, and subsystem functions such as sensor/surveillance, control, navigation and communications. This has major implications on the analytic model requirements, especially in regard to modeling scope, resolution (or fidelity), and computational expense. The objective of this thesis is to formulate and demonstrate improvements in modeling and simulation of fully integrated UAS to enable systems analysis across the levels of abstraction and multiple disciplines. This work also quantitatively characterizes collision avoidance as a critical element of separation assurance in terms of system behaviors across the levels of abstraction and multiple disciplines. To address these objectives, this thesis contributes to four areas: (1) a statistical gain-scheduling method to improve computational efficiency without a loss of accuracy or fidelity, (2) a hybrid collision avoidance algorithm using a machine learning technique that improves computational runtime as well as optimal trajectory cost, (3) a two-layer obstacle avoidance algorithm for a multi-obstacle environment, (4) a rapid, data-driven and grid-based urban modeling methodology using airborne LiDAR sources. The proposed modeling and simulation capability provides insights into the interaction between system of systems, systems, and subsystems that cannot be characterized by a conventional modeling and simulation environment. To illustrate the collision avoidance problem, this thesis examines the navigation of a fixed wing UAV in a dense urban environment.Ph.D