Assistant Professor
College of Science and Engineering (S&E)
Texas A&M University - Corpus Christi (TAMUCC)

Unmanned Systems Laboratory (TAMUCC-USL)

Faculty Associate
The Innovation in Computing Research Labs (iCORE)

Email: luis.garcia @
Tel: +1 (361) 825-3652

Postal address:
Texas A&M University - Corpus Christi
6300 Ocean Drive, Unit 5797
Corpus Christi, Texas 78412-5797
quick links



Luis Rodolfo GARCIA CARRILLO was born in Gomez Palacio, Durango, Mexico in 1980. He received the B.S. degree in Electronic Engineering in 2003, and the M.S. degree in Electrical Engineering in 2007, both from the Institute of Technology of La Laguna, Coahuila, Mexico. He received the Ph.D. degree in Control Systems from the University of Technology of Compiegne, France, in 2011, where he was advised by Professor Rogelio Lozano. From 2012 to 2013, he was a Postdoctoral Researcher at the Center for Control, Dynamical systems and Computation (CCDC) at the University of California, Santa Barbara, where he was working with Professor Joao Hespanha. During this time he was also a Researcher in the Institute for Collaborative Biotechnologies (ICB). He moved to Texas A&M University - Corpus Christi (TAMUCC), in 2013, where he currently holds an Assistant Professor position with the College of Science and Engineering. Prof. GARCIA CARRILLO is the Director of the Unmanned Systems Laboratory (TAMUCC-USL), and a Faculty Associate for the Innovation in Computing Research Labs (iCORE).

His research interests include control theory, dynamic modeling and control of Unmanned Aerial Vehicles (UAVs), computer vision, the use of vision in feedback control, intelligent systems, and multi-agent systems.

Currently Dr. GARCIA CARRILLO is working on applications concerning the dynamic modeling and control of UAVs, cooperative control, multi-agent systems, and vision-based target tracking.

Detailed Curriculum Vitae.


Dr. GARCIA CARRILLO research interests include control theory, computer vision, the use of vision in feedback control, optimal estimation, multi-agent systems, and modeling and control of UAVs.

Currently Dr. GARCIA CARRILLO is working on applications concerning Unmanned Aerial Vehicles (UAVs) development, vision-based target tracking, optimal estimation, cooperative control, and multi-agent systems.


The expression autonomous agents refers to ground, aerial or aquatic robots that perform tasks that require a significant amount of information gathering, data processing, and decision making, without explicit human control. These systems have found the most use in environments that are unaccessible or hazardous to humans, such as outer space, underwater, and battlefields. Autonomous agents are in a constant technological revolution, achieving different levels of autonomy mainly dictated by their capacity to sense and interact with their surrounding environment. As processors, sensors, actuators, and communication devices have increased in performance and decreased in cost, robotic platforms are becoming an integral part of our every day life. Therefore, the development of estimation and control algorithms that can guarantee a safe and efficient operation represent a key technology advancement needed for these robots to gain acceptance in society. My research aims at contributing with novel techniques for increasing the autonomy of robotic platforms, as well as enabling groups of robots to perform complex tasks in a cooperative fashion.


Bio-inspired Global Video Tracking By Networks of Unmanned Aircraft Systems

A collaborative work with the Institute for Collaborative Biotechnologies (ICB) at the University of California Santa Barbara, as well as with Toyon Research (small business performing technology development and defense systems analysis), addresses the development of bio-inspired estimation algorithms. This research explores the application of convex optimization methods to multi-agent systems with the main objective of allowing a group of camera-equipped UAVs to cooperatively estimate the position of target agents moving on ground. The solution proposed is based on a sum of norms minimization, which makes it appropriate for dealing with problems encountered in vision-based sensing and multi-agent systems e.g., impulsive noise, disturbances, and measurement outliers.

The picture on the left shows a target tracking scenario considering a pair of UAVs (labeled UAV 1 and UAV 2) cooperatively tracking a target vehicle (labeled TARGET) moving on ground, with respect to a reference coordinate system (labeled AGENT 0). The estimation problem can be represented by a graph where the nodes correspond to the states xi, i={0, 1, 2, 3} of the four agents AGENT 0, UAV 1, UAV 2, TARGET at 5 consecutive time instants {t0, t1, t2, t3, t4}. The edges of the graph correspond to measurements (dashed arrows) and models contraints (dash-dotted arrows). This example highlights the ability to represent heterogenous sensing: at times t1, t4 both UAVs acquire relative measurements between their positions and the target, at time t0 only UAV 2 can ''see'' the target, and at time t3 none of the UAVs ''sees'' the target; the UAVs only ''see'' each other at times t1, t2, t4.

This method was tested in successful field tests in April, July, and November 2012, at the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) in Camp Roberts, California.
Graph representation of a multi-agent estimation problem

Cluster Space Control of a Multi-Agent System for Escorting Formation

In this research, the task of enabling a multi-agent system for escorting a moving target in the context of cluster space control is addressed. The proposed trajectory generator for the cluster depends on the task proposed, which is defined according to the path to be followed, or the target position to be reached. The escorting problem is specified in terms of simple cluster parameter trajectories, resulting in basic robot motions satisfying the required formation behavior. A fuzzy logic controller has been designed in to deal with the uncertainty in the model of the platform used in our tests. Experimental results showing a team of 3 ground mobile robots escorting a moving target demonstrate the effectiveness of the proposed control scheme.
The control scheme implemented in this paper considers a
platoon of 3 mobile ground robots


Imaging sensors are very attractive since they are passive, non-contact, versatile, and low-cost. In addition, they can be used in situations where other sensing devices fail, leading to a whole new group of potential applications. My research has explored the conception and development of original vision-based sensing strategies for aerial robotic platforms.

3-dimensional Position and Velocity Regulation of a Quad-rotor Using Optical Flow

This research addresses the problem of enabling a quad-rotor UAV to perform the tasks of hover flight and translational velocity regulation with the main objective of allowing the vehicle to navigate autonomously. For this purpose, a vision system has been implemented in order to estimate the vehicle's relative altitude, lateral position, and forward velocity during flights. It is shown that, using visual information, it is possible to develop control strategies for different kinds of flying modes, such as hover flight and forward flight at constant velocity. A hierarchical control strategy is developed and implemented, and the local stability of the controller is also proven. Real-time experimental results consisting of autonomous hover and forward flight at constant velocity were successfully achieved, validating the proposed visual algorithm and control law.

Publications on this work can be found here.

Quad Rotorcraft Switching Control: An Application for the Task of Path Following

This research addresses the problem of road following using a quad-rotor equipped with an imaging system. The main objective consists of estimating and tracking a road without a priori knowledge of the path to be tracked, as well as obtaining efficient controllers for dealing with situations when the road is not detected in the camera's image. For this purpose, two operational regions are defined: one for the case when the road is detected, and the other for when it is not. Switching between the measurements of imaging and inertial sensors enables estimation of the required vehicle's parameters in both regions. Also, for dealing with both aforementioned cases, a switching control strategy which stabilizes the vehicle's lateral position is proposed. The system's stability is proved not only in the two regions, but also in the switching boundaries between them. The performance of the switching control is tested in real time experiments, successfully demonstrating the effectiveness of the proposed approach.

Publications on this work can be found here.


This research addresses the conception and development of autonomous quad rotorcraft experimental platforms. Commercially available quad-rotors do not allow the inclusion of novel sensing algorithms or controllers developed by the user. Thus, a quad-rotor platform was conceived and built, and was equipped with fully embedded autopilot and image processing, whose estimation, communication, and control algorithms were fully accessible. The vehicle was equipped with a sensor suit consisting of inertial sensors, imaging sensors (stereo and monocular vision system), altimetry sensor, and wireless communication links. A base station for monitoring the UAV was also conceived, which allowed retrieving and plotting the vehicle's states during autonomous flights.

Quad rotorcraft experimental platform with camera pointing downwards
Supervisory ground station. From left to right: Joystick, PC, 801.11n wireless link, XBEE09P data link
Quad rotorcraft experimental platform equipped with stereo imaging, inertial unit, and altitude sensing system


Control Systems, Sept, 2011.

M.Sc., Electrical Engineering, Sept, 2007.

B.S., Electronic Engineering, May, 2003.


Assistant Professor, July 2013 - Present
College of Science and Engineering
Texas A&M University - Corpus Christi, Texas

Director, July 2013 - Present
TAMUCC Unmanned Systems Laboratory (TAMUCC-USL)
Texas A&M University - Corpus Christi, Texas

Faculty Associate, August 2013 - Present
  The Innovation in Computing Research Labs (iCORE).
Texas A&M University - Corpus Christi, Texas

Postdoctoral Researcher
(Jr. Specialist) Feb, 2012 - June 2013
    Center for Control, Dynamical-systems, and Computation (CCDC), Institute for Collaborative Biotechnologies (ICB)
    Department of Electrical and Computer Engineering, University of California, Santa Barbara
               - Project: "GeoTrack: bio-inspired global video tracking by networks of unmanned aircraft systems"
               - Grant: W911NF-09-D-0001 from the U.S. Army Research Office (ARO)

Visiting Researcher
, Oct, 2011 - Jan, 2012
  French-Mexican Laboratory on Computer Science and Control. LAFMIA-UMI CNRS 3175. CINVESTAV - IPN, Mexico D.F., Mexico

Graduate Student Researcher, Sept, 2008 - Sept, 2011
    Heuristique et Diagnostic des Systemes Complexes (Heudiasyc)
    University of Technology of Compiegne. Compiegne, France


Journal Editor Board Member
International Program Committee Member
Reviewer for Scholarly Publications

Reviewer for Technical Meetings and Conferences


Assistant Professor
Texas A&M University - Corpus Christi, Corpus Christi, Texas
Teaching Assistant
, Sept 2008 - Sept 2011
    University of Technology of Compiegne. Compiegne, France

Robotics Mentor
, Sept 2012 - June 2013
    Santa Barbara High School. Santa Barbara, California


2014 National System of Researchers SNI - Level I (granted by CONACYT - Mexico)
    This award recognizes the work of a mexican researcher dedicated to the production of scientific knowledge and technology.


My Google Scholar page.

My ResearchGate page.