15 research outputs found
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
Path planning and control of flying robots with account of human’s safety perception
In this dissertation, a framework for planning and control of flying robot with the account of human’s safety perception is presented. The framework enables the flying robot to consider the human’s perceived safety in path planning. First, a data-driven model of the human’s safety perception is estimated from human’s test data using a virtual reality environment. A hidden Markov model (HMM) is considered for estimation of latent variables, as user’s attention, intention, and emotional state. Then, an optimal motion planner generates a trajectory, parameterized in Bernstein polynomials, which minimizes the cost related to the mission objectives while satisfying the constraints on the predicted human’s safety perception. Using Model Predictive Path Integral (MPPI) framework, the algorithm is possible to execute in real-time measuring the human’s spatial position and the changes in the environment.
A HMM-based Q-learning is considered for computing the online optimal policy. The HMM-based Q-learning estimates the hidden state of the human in interactions with the robot. The state estimator in the HMM-based Q-learning infers the hidden states of the human based on past observations and actions. The convergence of the HMM-based Q-learning for a partially observable Markov decision process (POMDP) with finite state space is proved using stochastic approximation technique.
As future research direction one can consider to use recurrent neural networks to estimate the hidden state in continuous state space. The analysis of the convergence of the HMM-based Q-learning algorithm suggests that the training of the recurrent neural network needs to consider both the state estimation accuracy and the optimality principle
Large bichromatic point sets admit empty monochromatic 4-gons
We consider a variation of a problem stated by Erd˝os
and Szekeres in 1935 about the existence of a number
fES(k) such that any set S of at least fES(k) points in
general position in the plane has a subset of k points
that are the vertices of a convex k-gon. In our setting
the points of S are colored, and we say that a (not necessarily
convex) spanned polygon is monochromatic if
all its vertices have the same color. Moreover, a polygon
is called empty if it does not contain any points of
S in its interior. We show that any bichromatic set of
n ≥ 5044 points in R2 in general position determines
at least one empty, monochromatic quadrilateral (and
thus linearly many).Postprint (published version
Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory
Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and
artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems