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

Multi-UAV Integrated Internet of Things System for Generating Safe Map in Post-Disaster

芝浦工業大学2019年

Exact and heuristic algorithms for multi-robot system routing, oriented to underwater monitoring. ​

The exploration of the underwater environment has always been a relevant field for science and technology, to enlarge our knowledge of this mainly unexplored environment. In this work, we apply a vehicle routing optimization method for underwater exploration and monitoring based on a fleet of small autonomous underwater vehicles (AUVs). We assume a coarse-grained map is already available from satellite measurements and the set of robots is used to get detailed information on sea bottom features. We provide exact and heuristic linear programming methods for finding both the optimal starting position and path planning for a fleet of drones. To obtain a realistic model useful in real applications, we enhance our formulation by imposing connectivity constraints among the AUVs. Lastly, we present a use case application for coral reef monitoring with real data taken by Abu Dhabi environmental authorities.The exploration of the underwater environment has always been a relevant field for science and technology, to enlarge our knowledge of this mainly unexplored environment. In this work, we apply a vehicle routing optimization method for underwater exploration and monitoring based on a fleet of small autonomous underwater vehicles (AUVs). We assume a coarse-grained map is already available from satellite measurements and the set of robots is used to get detailed information on sea bottom features. We provide exact and heuristic linear programming methods for finding both the optimal starting position and path planning for a fleet of drones. To obtain a realistic model useful in real applications, we enhance our formulation by imposing connectivity constraints among the AUVs. Lastly, we present a use case application for coral reef monitoring with real data taken by Abu Dhabi environmental authorities