Date of Award
Doctor of Philosophy (PhD)
Mechanical Engineering & Applied Mechanics
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
• 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
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.
Keller, James F., "Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles" (2016). Publicly Accessible Penn Dissertations. 2381.