Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143122/1/1.I010513.pd

Safe2Ditch Autonomous Crash Management System for Small Unmanned Aerial Systems: Concept Definition and Flight Test Results

Small unmanned aerial systems (sUAS) have the potential for a large array of highly-beneficial applications. These applications are too numerous to comprehensively list, but include search and rescue, fire spotting, precision agriculture, etc. to name a few. Typically sUAS vehicles weigh less than 55 lbs and will be performing flight operations in the National Air Space (NAS). Certain sUAS applications, such as package delivery, will include operations in the close proximity of the general public. The full benefit from sUAS is contingent upon the resolution of several technological areas in order to provide an acceptable level of risk for widespread sUAS operations. Operations of sUAS vehicles pose risks to people and property on the ground as well as manned aviation. Several of the more significant sUAS technological areas include, but are not limited to: autonomous sense and avoid and deconfliction of sUAS from other sUAS and manned aircraft, communications and interfaces between the vehicle and human operators, and the overall reliability of the sUAS and constituent subsystems. While all of the technological areas listed contribute significantly to the safe execution of the sUAS flight operations, contingency or emergency systems can greatly contribute to sUAS risk mitigations to manage situations where the vehicle is in distress. The Safe2Ditch (S2D) system is an autonomous crash management system for sUAS. Its function is to enable sUAS to execute emergency landings and avoid injuring people on the ground, damaging property, and lastly preserving the sUAS and payload. A sUAS flight test effort was performed to test the integration of sub-elements of the S2D system with a representative sUAS multi-rotor

Emergency Planning for Aerial Vehicles by Approximating Risk with Aerial Imagery and Geographic Data

Presented at the AIAA SCITECH 2022 ForumUrban Air Mobility and Advanced Air Mobility require the certification of novel electrified, vertical takeoff and landing, and autonomous aerial vehicles. These vehicles will operate at lower altitudes, in more dense environments, and with limited recovery abilities. Therefore, emergency landing scenarios must be considered broadly to understand the risks in some situations of flight failures. This work provides a preflight planning tool to assist these vehicles when emergency landing is required in crowded environments by fusing geographic data about the population, geometric data from lidar scans, and semantic data about land cover from aerial imagery. The Pix2Pix Conditional GAN is trained on Washington D.C. datasets to predict eight classifications at a 1m resolution. The information from this detection phase is transformed into a costmap, or riskmap, to use in planning the path to the safest landing locations. Multiple combinations of the cost layers are investigated in three test scenarios. The Rapidly Exploring Random Tree (RRT) algorithm efficiently searches for an alternative path that minimizes risk during emergency landing. The tool is demonstrated through three scenarios in the D.C. area. The objective is that the tool allows for the safe operation of UAM and AAM vehicles through crowded regions by providing confidence to the local population and federal regulators

Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model

Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140665/1/1.I010280.pd

Metalevel Motion Planning for Unmanned Aircraft Systems: Metrics Definition and Algorithm Selection

A diverse suite of manned and unmanned aircraft will occupy future urban airspace. Flight plans must accommodate specific aircraft characteristics, including physical volume with safety zone clearance, landing/takeoff procedures, kinodynamics, and a wide range of flight environments. No single motion planner is applicable across all possible aircraft configurations and operating conditions. This dissertation proposes the first motion planning algorithm selection capability with application to small Unmanned Aircraft System (UAS) multicopters operating in and over a complex urban landscape. Alternative data-driven fail-safe protocols are presented to improve on contemporary ``fly-home'' or automatic landing protocols, focusing on rooftops as safe urban landing sites. In a fail-safe direct strategy, the multicopter identifies, generates, and follows a flight plan to the closest available rooftop suitable for landing. In a fail-safe supervisory strategy, the multicopter examines rooftops en route to a planned landing site, diverting to a closer, clear landing site when possible. In a fail-safe coverage strategy, the multicopter cannot preplan a safe landing site due to missing data. The multicopter executes a coverage path to explore the area and evaluate overflown rooftops to find a safe landing site. These three fail-safe algorithms integrate map generation, flight planning, and area coverage capabilities. The motion planning algorithm selection problem (ASP) requires qualitative and quantitative metrics to inform the ASP of user/agent, algorithm, and configuration space preferences and constraints. Urban flight map-based, path-based, and software-based cost metrics are defined to provide insights into the urban canyon properties needed to construct safe and efficient flight plans. Map-based metrics describe the operating environment by constructing a collection of GPS/Lidar navigation performance, population density, and obstacle risk exposure metric maps. Path-based metrics account for a vehicle's energy consumption and distance traveled. Software-based metrics measure memory consumption and execution time of an algorithm. The proposed metrics provide pre-flight insights typically ignored by obstacle-only planning environment definitions. An algorithm portfolio consisting of geometric (Point-to-Point: PTP), graph-based (A* variants), and sampling-based (BIT* variants) motion planners were considered in this work. Path cost, execution time, and success rate benchmarks were investigated using Monte Carlo problem instances with A* "plus" producing the lowest cost paths, PTP having the fastest executions, and A* "dist" having the best overall success rates. The BIT* variant paths typically had higher cost but their success rate increased relative to altitude. The problem instances and metric maps informed two new machine learning solutions for urban small UAS motion planning ASP. Rule-based decision trees were simple to construct but unable to capture both complex cost metrics and algorithm properties. The investigated neural network-based ASP formulations produced promising results, with a hybrid two-stage selection scheme having the best algorithm selection accuracy, laying the seeds for future work. The most significant innovation of this dissertation is motion planning ASP for UAS. Non-traditional open-source databases also advance the field of data-driven flight planning, contributing to fail-safe UAS operations as well as ASP. Path planning algorithms integrated a new suite of diverse cost metrics accompanied by a novel multi-objective admissible heuristic function. Neural network and decision tree ASP options were presented and evaluated as a first-case practical approach to solving the motion planning ASP for small UAS urban flight.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168060/1/cosme_1.pd

COMBINED ROBUST OPTIMAL DESIGN, PATH AND MOTION PLANNING FOR UNMANNED AERIAL VEHICLE SYSTEMS SUBJECT TO UNCERTAINTY

Unmanned system performance depends heavily on both how the system is planned to be operated and the design of the unmanned system, both of which can be heavily impacted by uncertainty. This dissertation presents methods for simultaneously optimizing both of these aspects of an unmanned system when subject to uncertainty. This simultaneous optimization under uncertainty of unmanned system design and planning is demonstrated in the context of optimizing the design and flight path of an unmanned aerial vehicle (UAV) subject to an unknown set of wind conditions. This dissertation explores optimizing the path of the UAV down to the level of determining flight trajectories accounting for the UAVs dynamics (motion planning) while simultaneously optimizing design. Uncertainty is considered from the robust (no probability distribution known) standpoint, with the capability to account for a general set of uncertain parameters that affects the UAVs performance. New methods are investigated for solving motion planning problems for UAVs, which are applied to the problem of mitigating the risk posed by UAVs flying over inhabited areas. A new approach to solving robust optimization problems is developed, which uses a combination of random sampling and worst case analysis. The new robust optimization approach is shown to efficiently solve robust optimization problems, even when existing robust optimization methods would fail. A new approach for robust optimal motion planning that considers a “black-box” uncertainty model is developed based off the new robust optimization approach. The new robust motion planning approach is shown to perform better under uncertainty than methods which do not use a “black-box” uncertainty model. A new method is developed for solving design and path planning optimization problems for unmanned systems with discrete (graph-based) path representations, which is then extended to work on motion planning problems. This design and motion planning approach is used within the new robust optimization approach to solve a robust design and motion planning optimization problem for a UAV. Results are presented comparing these methods against a design study using a DOE, which show that the proposed methods can be less computationally expensive than existing methods for design and motion planning problems