Devendra P Garg


Professor Emeritus in the Department of Mechanical Engineering and Materials Science

Professor Garg's areas of interest include modeling, simulation, and control of dynamic systems and robotics. In particular, his research deals with characterization and control of nonlinear phenomena in physical systems such as robots, automated manufacturing, and high-speed ground transportation. In the area of robotics, he is interested in the design of feedback controllers using the concepts of modern control theory. In the area of high-speed ground transportation, he has conducted research on dynamics and control of ground-based, air-cushion and magnetically levitated vehicles.

One challenging area of research which Professor Garg is currently
pursuing deals with the coordination and control of two robots
handling a large structural object performing a series of intricate
maneuvers in a confined work space. Analytical development of path
planning and collision avoidance strategies and their practical
implementation are being carried out in the Robotics
and Manufacturing Automation (RAMA)
laboratory. Two ABB Industrial
grade six-degree-of freedom revolute jointed robots are available
for these experiments. Major challenges related to this research
effort include the identification and design of techniques for
incorporating sensory data in the control algorithm, modeling the
nonlinear dynamics of the manipulators, and the development of
intelligent and adaptive control schemes for the coordination of
multiple arms in the presence of unknown parameters and payload
variations. Another area of research that Professor Garg is actively pursuing is sensor modeling, data acquisition and management, and data fusion in the context of swarm robotics. For the experimental work in this area, the Robot Control Laboratory has a number of KheperaII robots, LADAR sensors, and vision sensors.

Appointments and Affiliations

  • Professor Emeritus in the Department of Mechanical Engineering and Materials Science

Contact Information

  • Office Location: 223 Hudson Hall, Durham, NC 27708
  • Office Phone: (919) 660-5330
  • Email Address:


  • Ph.D. New York University, 1969
  • M.S. University of Wisconsin - Madison, 1960
  • B.S.E. University of Roorkee (India), 1957
  • B.S. Agra University (India), 1954

Research Interests

The main emphasis of research in the Robotics and Manufacturing Automation Laboratory is on the control of multiple robots that can work together. Multiple robotic control of the two ABB industrial robotic arms was sponsored by the National Science Foundation. The sensor modeling, data acquisition, data management, and sensor fusion research was being funded by the Army Research Office and the National Science Foundation. We are currently exploring ways of using neural networks and fuzzy logic algorithms incorporated in the robot control strategies. In addition, we are emphasizing swarm intelligence and control in our research using a network of small-size mobile robots. The research is primarily inspired by the existence of very robust biological counterparts such as swarming in ants, flocking of birds, and schooling in fishes. The primary feature of these systems that has attracted researchers is that the intelligence associated with an individual agent (e.g., ant or bird) is very primitive, and it utilizes interactions at local level to arrive at very simple decisions. This behavior at local level emerges into a group behavior that appears to be very robust and complex. This observation in biological systems has led the interested scientists and engineers to investigate multiple agent cooperative controls problem using a bottom-up approach. We have developed a multiple mobile robot test bed equipped with a Cognex-5400 camera, 2 SICK LADAR sensors, and 8 KheperaII mobile robots. These mobile robots are controlled via a radio controller through a desk-top computer. We are also working on developing formation control algorithms and strategies. The next step will be to implement these algorithms on our test-bed. The control of robotic devices via the internet has become an increasingly important area of research in the last few years. We have created a web based interface for our ABB IRB 140 industrial arms that provided the user with various functionalities, such as moving the robot arms linearly to specified coordinate offsets, opening and closing the grippers, rotating the tool (gripper) about the three axes (x, y and z), accessing the F/T values, and moving the conveyor and the indexing table. The major emphasis of our research on work cell simulation is on machine tools and related hardware operating in flexible manufacturing work cells. Past problems and recent advances, and guidelines for work cell design were also looked at. Two flexible manufacturing work cell models were created, which are capable of manufacturing a certain part. The costs of each of the layouts were compared with the costs of manufacturing the part to determine the optimal layout. Our research work in sensor fusion involves development of formal approaches to capture uncertainties inherent in sensor measurements in the form of appropriate probabilistic and analytical sensor models, and use those models to fuse data from multiple sources. The uncertainties involved in sensor measurements can arise from each sensor's limitations, change in environmental parameters, or performance of estimation/calibration algorithm (such as image processing algorithm in case of vision sensor). The research focuses on developing a unified approach to capture uncertainties arising from any possible source in the form of sensor models, and involves the use of multiple vision sensors, infra-red sensors, and sonar ranging sensors .

Awards, Honors, and Distinctions

  • Edwin F. Church Medal. American Society of Mechanical Engineers. 2003

Representative Publications

  • Zhang, G; Fricke, GK; Garg, DP, Spill detection and perimeter surveillance via distributed swarming agents, Ieee/Asme Transactions on Mechatronics, vol 18 no. 1 (2013), pp. 121-129 [10.1109/TMECH.2011.2164578] [abs].
  • Young, R; Garg, D, Potential Function Control of Distributed Multi-agent Systems, Journal of Robotics (2011) [abs].
  • Milutinović, DL; Garg, DP, Kalman smoother based force localization and mapping using intravital video microscopy, Journal of Dynamic Systems, Measurement, and Control, vol 132 no. 6 (2010) [10.1115/1.4002485] [abs].
  • Kumar, M; Garg, DP; Kumar, V, Segregation of heterogeneous units in a swarm of robotic agents, Ieee Transactions on Automatic Control, vol 55 no. 3 (2010), pp. 743-748 [10.1109/TAC.2010.2040494] [abs].
  • Kumar, M; Garg, D, Intelligent Sensor Uncertainty Modeling Techniques and Data Fusion, International Journal of Control and Intelligent Systems, vol 37 no. 2 (2009), pp. 67-77 [abs].

Additional Information

Professional Society Service:

  • American Academy of Mechanics
  • The New York Academy of Sciences
  • The Society of the Sigma Xi
  • American Society of Mechanical Engineers