Landing site reachability and decision making for UAS forced landings

After a huge amount of success within the military, the benefits of the use of unmanned aerial systems over manned aircraft is obvious. They are becoming cheaper and their functions advancing to such a point that there is now a large drive for their use by civilian operators. However there are a number of significant challenges that are slowing their inevitable integration into the national airspace systems of countries. A large array of emergency situations will need to be dealt with autonomously by contingency management systems to prevent potentially deadly incidences. One such emergency situation that will need autonomous intervention, is the total loss of thrust from engine failure. The complex multi faceted task of landing the stricken aircraft at a potentially unprepared site is called a forced landing. This thesis presents methods to address a number of critical parts of a forced landing system for use by an unmanned aerial system. In order for an emergency landing site to be considered, it needs to be within glide range. In order to find a landing site s reachability from the point of engine failure the aircraft s glide performance and a glide path must be known. A method by which to calculate the glide performance, both from aircraft parameters or experiments is shown. These are based on a number of steady state assumptions to make them generic and quick to compute. Despite the assumptions, these are shown to have reasonable accuracy. A minimum height loss path to the landing site is defined, which takes account of a steady uniform wind. While this path is not the path to be flown it enables a measure of how reachable a landing site is, as any extra height the aircraft has once it gets to the site makes a site more reachable. It is shown that this method is fast enough to be run online and is generic enough for use on a range of aircraft. Based on identified factors that make a landing site more suitable, a multi criteria decision making Bayesian network is developed to decide upon which site a unmanned aircraft should land in. It can handle uncertainty and non-complete information while guaranteeing a fast reasonable decision, which is critical in this time sensitive situation. A high fidelity simulation environment and flight test platform are developed in order to test the performance of the developed algorithms. The test environments developed enable rapid prototyping of algorithms not just within the scope of this thesis, but on a range of vehicle types. In simulation the minimum height loss paths show good accuracy, for two completely different types of aircraft. The decision making algorithms show that they are capable of being ran online in a flight test. They make a reasonable decision and are capable of quickly reacting to changing conditions, enabling redirection to a more suitable landing site

Robust Trajectory Planning for Autonomous Parafoils under Wind Uncertainty

A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into di cult, complex terrain. Robustness is a primary concern, given that environmental wind disturbances are often highly uncertain and time-varying, coupled with under-actuated dynamics and potentially narrow drop zones. This paper presents a new on-line trajectory planning algorithm that enables a large, autonomous parafoil to robustly execute collision avoidance and precision landing on mapped terrain, even with signi cant wind uncertainties. This algorithm is designed to handle arbitrary initial altitudes, approach geometries, and terrain surfaces, and is robust to wind disturbances which may be highly dynamic throughout the terminal approach. Explicit, real-time wind modeling and classi cation is used to anticipate future disturbances, while a novel uncertainty-sampling technique ensures that robustness to possible future variation is e ciently maintained. The designed cost-to-go function enables selection of partial paths which intelligently trade o between current and reachable future states. Simulation results demonstrate that the proposed algorithm reduces the worst-case impact of wind disturbances relative to state-of-the-art approaches.Charles Stark Draper Laborator

Robust Trajectory Planning for Autonomous Parafoils under Wind Uncertainty

A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into di cult, complex terrain. Robustness is a primary concern, given that environmental wind disturbances are often highly uncertain and time-varying, coupled with under-actuated dynamics and potentially narrow drop zones. This paper presents a new on-line trajectory planning algorithm that enables a large, autonomous parafoil to robustly execute collision avoidance and precision landing on mapped terrain, even with signi cant wind uncertainties. This algorithm is designed to handle arbitrary initial altitudes, approach geometries, and terrain surfaces, and is robust to wind disturbances which may be highly dynamic throughout the terminal approach. Explicit, real-time wind modeling and classi cation is used to anticipate future disturbances, while a novel uncertainty-sampling technique ensures that robustness to possible future variation is e ciently maintained. The designed cost-to-go function enables selection of partial paths which intelligently trade o between current and reachable future states. Simulation results demonstrate that the proposed algorithm reduces the worst-case impact of wind disturbances relative to state-of-the-art approaches.Charles Stark Draper Laborator

Certified Reinforcement Learning with Logic Guidance

This paper proposes the first model-free Reinforcement Learning (RL) framework to synthesise policies for unknown, and continuous-state Markov Decision Processes (MDPs), such that a given linear temporal property is satisfied. We convert the given property into a Limit Deterministic Buchi Automaton (LDBA), namely a finite-state machine expressing the property. Exploiting the structure of the LDBA, we shape a synchronous reward function on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces that probabilistically satisfy the linear temporal property. This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property. Under the assumption of finite state space, theoretical guarantees are provided on the convergence of the RL algorithm to an optimal policy, maximising the above probability. We also show that our method produces ''best available'' control policies when the logical property cannot be satisfied. In the general case of a continuous state space, we propose a neural network architecture for RL and we empirically show that the algorithm finds satisfying policies, if there exist such policies. The performance of the proposed framework is evaluated via a set of numerical examples and benchmarks, where we observe an improvement of one order of magnitude in the number of iterations required for the policy synthesis, compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782

Motion Planning in Artificial and Natural Vector Fields

This dissertation advances the field of autonomous vehicle motion planning in various challenging environments, ranging from flows and planetary atmospheres to cluttered real-world scenarios. By addressing the challenge of navigating environmental flows, this work introduces the Flow-Aware Fast Marching Tree algorithm (FlowFMT*). This algorithm optimizes motion planning for unmanned vehicles, such as UAVs and AUVs, navigating in tridimensional static flows. By considering reachability constraints caused by vehicle and flow dynamics, flow-aware neighborhood sets are found and used to reduce the number of calls to the cost function. The method computes feasible and optimal trajectories from start to goal in challenging environments that may contain obstacles or prohibited regions (e.g., no-fly zones). The method is extended to generate a vector field-based policy that optimally guides the vehicle to a given goal. Numerical comparisons with state-of-the-art control solvers demonstrate the method\u27s simplicity and accuracy. In this dissertation, the proposed sampling-based approach is used to compute trajectories for an autonomous semi-buoyant solar-powered airship in the challenging Venusian atmosphere, which is characterized by super-rotation winds. A cost function that incorporates the energetic balance of the airship is proposed to find energy-efficient trajectories. This cost function combines the main forces acting on the vehicle: weight, buoyancy, aerodynamic lift and drag, and thrust. The FlowFMT* method is also extended to consider the possibility of battery depletion due to thrust or battery charging due to solar energy and tested in this Venus atmosphere scenario. Simulations showcase how the airship selects high-altitude paths to minimize energy consumption and maximize battery recharge. They also show the airship sinking down and drifting with the wind at the altitudes where it is fully buoyant. For terrestrial applications, this dissertation finally introduces the Sensor-Space Lattice (SSLAT) motion planner, a real-time obstacle avoidance algorithm for autonomous vehicles and mobile robots equipped with planar range finders. This planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow an artificial vector field that encodes the desired vehicle path. This planner is evaluated in challenging, cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. Our results show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run-time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a significant speedup due to the independent collision checks

Where to Land: A Reachability Based Forced Landing Algorithm for Aircraft Engine Out Scenarios

No abstract availabl

Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.Comment: 34 pages, 2 figures, 3 table

Robust planning for autonomous parafoil

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 112-119).Parafoil trajectory planning systems must be able to accurately guide the highly non-linear, under-actuated parafoil system from the drop zone to the pre-determined impact point. Parafoil planning systems are required to navigate highly complex terrain scenarios, particularly in the presence of an uncertain and potentially highly dynamic wind environment. This thesis develops a novel planning approach to parafoil terminal guidance. Building on the chance-constrained rapidly exploring random tree (CC-RRT) [1] algorithm, this planner, CC-RRT with Analytic Sampling, considers the non-linear dynamics, as well as the under-actuated control authority of the parafoil by construction. Additionally, CC-RRT with Analytic Sampling addresses two important limitations to state-of-the-art parafoil trajectory planners: (1) implicit or explicit constraints on starting altitude of the terminal guidance phase, and (2) a reactive or limitedly-proactive approach to handling the eect of wind uncertainty. This thesis proposes a novel formulation for the cost-to-go function, utilizing an approximation of the reachability set for the parafoil to account for the eect of vehicle heading on potential future states. This cost-to-go function allows for accurate consideration of partially planned paths, effectively removing strict constraints on starting altitude of the terminal guidance phase. The reachability set cost-to-go function demonstrates considerably improved performance over a simple LQR cost function, as well as cost-to-go functions with a glide-slope cone bias, demonstrating the eectiveness of utilizing the reachability set approximation as a means for incorporating heading dynamics. Furthermore, this thesis develops a multi-class model for characterizing the uncertain effect of wind. The wind model performs an online classication based on the observed wind measurements in order to determine the appropriate level of planner conservatism. Coupling this wind model with the method for sampling the analytic uncertainty distribution presented in this thesis, the CCRRT with Analytic Sampling planner is able to eciently account for the future eect of wind uncertainty and adjust trajectory plans accordingly, allowing the planner to operate in arbitrary terrain configurations without issue. CC-RRT with Analytic Sampling performs exceptionally well in complex terrain scenarios. Simulation results demonstrate signicant improvement on complex terrain relative to the state-of-the-art Band-Limited Guidance (BLG) [2], drastically reducing the worst case and average target miss distances. Simulation results demonstrate the CC-RRT with Analytic Sampling algorithm remains un-affected as terrain complexity increases, making it an ideal choice for applications where difficult terrain is an issue, as well as missions with targets with drastically dierent terrain conditions. Moreover, CC-RRT with Analytic Sampling is capable of starting terminal guidance at significantly higher altitudes than conventional approaches, while demonstrating no signicant change in performance.by Ian Sugel.S.M