Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles

This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles (UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission, fixed wing vehicles have maneuver constraints that can limit their performance in this role. Current technology vehicles are capable of long duration flight with a minimal acoustic footprint while carrying an array of cameras and sensors. Both military tactical and civilian safety applications can benefit from this technology. We make three main contributions: C1 A sequential path planner that generates a C2 flight plan to persistently acquire a covering set of data over a user designated area of interest. The planner features the following innovations: • A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length • A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction • A smooth path generator that provides C2 routes that satisfy user specified curvature constraints C2 A set of algorithms to coordinate multiple UAVs, including mission commencement from arbitrary locations to the start of a coordinated mission and de-confliction of paths to avoid collisions with other vehicles and fixed obstacles iv C3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing validated through flight test experiments on multiple platforms. A variety of tests and platforms are discussed. The algorithms presented are based on a technical approach with approximately equal emphasis on analysis, computation, dynamic simulation, and flight test experimentation. Our planner (C1) directly takes into account vehicle maneuverability and agility constraints that could otherwise render simple solutions infeasible. This is especially important when surveillance objectives elevate the importance of optimized paths. Researchers have devel oped a diverse range of solutions for persistent surveillance applications but few directly address dynamic maneuver constraints. The key feature of C1 is a two stage sequential solution that discretizes the problem so that graph search techniques can be combined with parametric polynomial curve generation. A method to abstract the kino-dynamics of the aerial platforms is then presented so that a graph search solution can be adapted for this application. An A* Traveling Salesman Problem (TSP) algorithm is developed to search the discretized space using the abstract distance metric to acquire more data or avoid obstacles. Results of the graph search are then transcribed into smooth paths based on vehicle maneuver constraints. A complete solution for a single vehicle periodic tour of the area is developed using the results of the graph search algorithm. To execute the mission, we present a simultaneous arrival algorithm (C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and to ensure there are no collisions at any of the path intersections. We present a toolbox of spline-based algorithms (C3) to streamline the development of C2 continuous paths with numerical stability. These tools are applied to an aerial persistent surveillance application to illustrate their utility. Comparisons with other parametric poly nomial approaches are highlighted to underscore the benefits of the B-spline framework. Performance limits with respect to feasibility constraints are documented

Coverage Path Planning for a Moving Vehicle

A simple coverage plan called a Conformal Lawn Mower plan is demonstrated. This plan enables a UAV to fully cover the route ahead of a moving ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. Necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set are established. In simulation, the proposed plan is validated, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. The results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle. Next, I investigate the challenges involved in providing timely mapping information to a moving ground vehicle where the path of that vehicle is not known in advance. I establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path the ground vehicle may follow. Finally, I consider a reduced problem for sensor coverage ahead of a moving ground vehicle. Given the ground vehicle route, the UAV planner calculates the regions that must be covered and the time by which each must be covered. The UAV planning problem takes the form of an Orienteering Problem with Time Windows (OPTW). The problem is cast the problem as a Mixed Integer Linear Program (MILP) to find a UAV path that maximizes the area covered within the time constraints dictated by the moving ground vehicle. To improve scalability of the proposed solution, I prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. This divide and conquer strategy allows faster solution times, and also provides higher-quality solutions when given a fixed time budget for solving the MILP. We also demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss

Mission-Oriented Autonomy for Intelligent, Adaptive, and Multi-Agent Remote Sensing of Ice Sheets using Unmanned Aerial Systems

Throughout our history, humanity has been developing and progressing technology in order to help us better understand the world in which we live. As climate change becomes an increasingly urgent global crisis, scientists have been tasked with developing models for better understanding the complex dynamics involved, as well as to more accurately forecast the long term effects on our environment. With respect to sea level rise, both our knowledge of these dynamics and the accuracy of these models can be improved through the routine collection of crucial data concerning glacier ice thickness and bedrock topology. To accomplish this, innovative solutions are being developed by groups of inter-disciplinary research teams, combining fields such as earth-science, radar systems, data science, and aerospace engineering. Through this collaboration, we have the potential to leverage breakthroughs in unmanned systems technology and miniaturized, specialized sensors for comprehensive, precise, and routine data collection of key polar research objectives. As Unmanned Aerial Systems (UASs) have become more reliable research platforms in recent years, they now have the capability to perform these remote sensing operations at a reduced cost compared to manned operations, while also providing repeatable, precision tracking capabilities along flight lines, enabling the surveying of tightly-spaced grids, and removing human flight crews from hazardous polar environments. However, the payload, range, and wind constraints for these platforms severely restrict their operational sensing footprint. Additionally, UASs generally have a much smaller wingspan compared to manned aircraft typically used in Earth Science missions, which becomes a challenging factor for incorporating efficient directive antennas at the low operating frequencies required for glacial sounding. The aim of this work is to address these issues and to enhance mission efficiency and the overall quality of data collection for these operations through the implementation of onboard mission-oriented autonomy that includes cognitive decision-making for intelligent survey operations, adaptive functionalities, and a scalable, robust framework for multi-agent operations. As opposed to conventional methods for polar research operations which generally involve single-agent missions, using standard waypoint guidance and fixed-routes planned by human operators, the unique contributions of the developed mission-oriented autonomy in this work include: 1) Automated flight line generation for rapid and reliable mission planning of tightly-spaced flight lines required for cross-track synthetic aperture radar processes and surface clutter suppression, with required spacing based on the operating frequency of the onboard radar system. 2) Implementation of Dubins Path guidance methods into polar research operations for precision end-to-end survey of mission flight lines while taking into account the kinematic constraints of the fixed wing aircraft, as well as for efficiently traversing to and from a home loiter location during mission operations. 3) Cognitive, real-time optimal path planning through mission flight lines utilizing both deterministic and stochastic Traveling Salesman Problem heuristics. 4) Modifications to these Traveling Salesman Problem heuristics for ensuring safe, feasible, and reliable operations in real-time by taking into account aircraft range constraints. 5) Collaborative Multi-Agent survey operations utilizing space partitioning and Hungarian Assignment for distributed task allocation, as well as morphing potential fields for collision avoidance. 6) Modifications for Multi-Agent deployment scheduling to reduce inter-agent interference for sensitive radar systems to improve coherency of the collected data, and to rapidly and efficiently deploy agents into and out of survey areas. 7) Modifications for Heterogeneous flight operations for increasing operational capabilities through cross-platform collaboration. 8) Failsafe features to instill robustness in Multi-Agent operations with respect towards accommodating and adapting to single-agent system failures, by automatically re-planning collaborative survey operations. In this work, the motivation for the creation of this mission-oriented autonomy is discussed, along with the methodology of each of the autonomy features, and the framework for implementation onto UAS platforms. Case studies are conducted for past and future polar research deployments using unmanned systems to assess the potential improvements in operational capabilities and data collection for the developed autonomy compared to conventional methods. Finally, the developed autonomy is implemented onto an embedded system for preliminary flight testing and validation, as well as used for intelligent mission planning for a manned operation