Assistant Professor
School of Engineering and Computing Sciences (ENCS)
Texas A&M University - Corpus Christi (TAMUCC)

Unmanned Systems Laboratory (TAMUCC-USL)

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 School of Engineering and Computing Sciences. Prof. GARCIA CARRILLO is the Director of the Unmanned Systems Laboratory (TAMUCC-USL), and an Executive Board Member with the Innovation in Computing Research Labs (iCORE).

His research interests include control theory, multi-agent control systems, Unmanned Aircraft Systems (UAS), embedded systems, and the use of vision in feedback control.

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

Detailed Curriculum Vitae.


Dr. GARCIA CARRILLO research interests include control theory, multi-agent systems, Unmanned Aircraft Systems (UAS), embedded systems, and the use of vision in feedback control.

Currently Dr. GARCIA CARRILLO is working on applications concerning cooperative control, multi-agent systems, adaptive systems, and dynamic modeling and control of UASs.


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.


This research is focused on developing novel and systematic methodologies for the analysis, design, and implementation of cooperative dynamic networks. A dynamic network consists of spatially distributed dynamic nodes (e.g., autonomous vehicles, mobile sensors) which are coordinated by common set of goals and possible dynamic interaction between the nodes. There are many applications where a dynamic network may be more suitable than a single vehicle, especially where a distributed system of sensors is advantageous. Yet, without coordinating the movement of the nodes or without managing the information the nodes retrieve, any advantage of dynamic network deployment may be lost and damaging collisions or interference may occur.

A group of two UAS performing a coordinated target tracking mission A group of three UAS performing a coordinated target tracking mission

A group of UAS performing a non-coordinated target tracking mission

Graph representation of a multi-agent estimation problem
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.


Autonomous rotorcrafts are often used to survey disasters like the Fukushima meltdown, and the earthquake in Nepal. Right now they can only fly horizontally, making their pathway quite limited. But what if they could rotate 90 degrees into the vertical? We could use this platform and make it through a doorway and to be able to look for people inside of a building without putting conditional risk of human life in there. Not only that, but if a motor or blade fails the drone could still fly unlike those being used today.

Our research explores the design of a morphing multi-rotorcraft that is capable of overcoming the challenges previously explained. The proposed vehicle has eight rotors and is able to fly in a conventional horizontal flight configuration (similar to a quad rotorcraft), as well as in a vertical body flight configuration. During the vertical flight configuration the UAS will be capable of operate with only four rotors, which is very useful for overcoming rotor failure conditions. The vertical flight mode will also enable the UAS to enter narrow areas if needed. In search and rescue operations, both indoors and outdoors, these characteristics may be very valuable. The Figure to the left presents the expected functionality of the morphing fault tolerant rotorcraft.


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.


Fixed-wing UAS for precision agriculture tasks

It is estimated that global food production must double from current output in order to meet the demands of a global population projected to reach 9 billion by the year 2050. To meet this demand, the global agriculture industry must significantly increase crop yields and reduce crop loss. Much research is being focused on precision farming as a partial solution to yield enhancement. Precision farming relies on new technologies (including remote sensing) to gather and evaluate data at the field level to support agronomic management decisions throughout the growing season to improve crop yields. Neither satellite sensors nor manned aircraft provide the granularity or responsiveness needed to be effective platforms for agricultural remote sensing. Given the significant advances in technology, sensor-equipped unmanned aerial systems (UAS) could potentially serve as responsive, flexible, and cost-effective platforms for remote sensing to support precision farming and other agricultural applications.

This research addresses the conception and development of an autonomous fixed-wing UAS for precision agriculture tasks. The vehicle is equipped with a sensor suit consisting of inertial sensors, imaging sensors (stereo and monocular vision system), thermal imaging, altimetry sensor, and wireless communication links. A base station for monitoring the UAS is also available, which allows retrieving and plotting the vehicle's states as well as the data acquired during autonomous flights.

Quad-Rotorcraft UAS

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 UAS 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


Post-Doctoral Fellow, Control Systems, June 2013

PhD., Control Systems, Sept, 2011.

M.Sc., Electrical Engineering, Sept, 2007.
  • Institution: Institute of Technology of La Laguna, Coahuila, Mexico.
  • Dissertation Title: Application of vision techniques for automatic takeoff and landing of helicopters.
  • Adviser: Alejandro Dzul

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


Assistant Professor, July 2013 - Present
    School of Engineering and Computing Sciences
    Texas A&M University - Corpus Christi, Texas

July 2013 - Present
    TAMUCC Unmanned Systems Laboratory (TAMUCC-USL)
    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
  • Working with Professor Joao Hespanha in the area of optimal estimation and multi-agent systems.
               - 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
  • Project: "Picardie French Region Project, Energy and Transport Intermodality"
  • Research Subject: Autonomous Take-off and Landing of Unmanned Aerial Vehicles
  • Adviser: Rogelio Lozano


Journal Editor Board Member
International Program Committee Member
  • The International Conference on Unmanned Aircraft Systems, Denver, Colorado, USA, 2015.
  • The Sixth Annual IEEE Green Technologies Conference, Corpus Christi, Texas, USA, 2014.
  • IEEE Multi-Conference on Systems and Control, Hyderabad, India, 2013.
Reviewer for Scholarly Publications
  • IEEE Transactions on Neural Networks and Learning Systems (2015-present).
  • IEEE Transactions on Robotics (2014-present).
  • IEEE Transactions on Control of Network Systems (2014-present).
  • Elsevier - Automatica (2014-present).
  • Elsevier - Aerospace Science and Technology (2014-present)
  • Hindawi - Mathematical Problems in Engineering (2014-present).
  • SAGE Transactions of the Institute of Measurement and Control (2014-present).
  • IEEE Transactions on Automation Science and Engineering (2013-present).
  • IEEE Signal Processing Letters (2013-present).
  • Elsevier - Robotics and Autonomous Systems (2013-present).
  • Elsevier - Revista Iberoamericana de Automatica e Informatica Industrial (2013-present).
  • Wiley - Optimal Control Applications and Methods (2013-present).
  • Journal of Unmanned System Technology (2013-present).
  • International Journal of Advanced Robotic Systems (2012-present).
  • Journal of Mechanical Science and Technology (JMST) (2011-present).
  • Robotica (2011-present).
  • Journal of Intelligent and Robotic Systems (JIRS) (2010-present).

Reviewer for Technical Meetings and Conferences

  • IEEE Conference on Decision and Control, IEEE American Control Conference, IFAC World Congress, European Control Conference, IEEE Multi-Conference on Systems and Control (MSC), IEEE International Conference on Control & Automation, IEEE Mediterranean Conference on Control and Automation, IFAC Research, Education and Development of Unmanned Aerial Systems (RED-UAS).


Assistant Professor
    Texas A&M University - Corpus Christi, Corpus Christi, Texas
  • Currently Teaching: 
    • TBA
Teaching Assistant, Sept 2008 - Sept 2011
    University of Technology of Compiegne. Compiegne, France
  • Course: SIT58 - Control and observation of embedded real-time systems

Robotics Mentor, Sept 2012 - June 2013
    Santa Barbara High School. Santa Barbara, California
  • Course: Physics & Green Engineering - Robotics
  • Activities: Introducing students to the basics of robotics, computer programming, sensing devices, actuators, and development of simple control algorithms.


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.
  • The Mexican National Council of Science and Technology (CONACYT) it is the equivalent of USA’s National Science Foundation (NSF).


My Google Scholar page.

My ResearchGate page.