OAEE Faculty

Sofge, Don

Sofge, Don

Office of Advanced Engineering Education

Don Sofge is a Roboticist at the Naval Research Laboratory with 30 years of experience in Artificial Intelligence, Machine Learning, and Control Systems R&D. He leads the Distributed Autonomous Systems Group in the Laboratory for Autonomous Systems Research, where he develops nature-inspired computing paradigms to challenging problems in sensing, artificial intelligence, and control of autonomous robotic systems. His current research focuses on control of autonomous teams or swarms of heterogeneous robotic systems. He has served as an advisor on autonomous systems to DARPA, ONR, OSD, ARL, NSF, and NASA, as well as US representative on international TTCP and NATO technical panels on autonomous systems, and currently serves as a member of the Robotics and Intelligent Systems and Machine Learning and Artificial Intelligence Interagency Working Groups under the White House OSTP National Science and Technology Council.

Autonomous Teams, Nature-inspired Computing, Machine Learning, Intelligent Control, Multiagent Planning, Trust in Autonomous Systems

  • R. Mittu, D. Sofge, A. Wagner, and W. Lawless (Eds.), Robust Intelligence and Trust in Autonomous Systems, Springer, 2016.
  • N. Sydney, D. Paley, and D. Sofge, “Physics-Inspired Motion Planning for Information-Theoretic Target Detection using Multiple Aerial Robots” Autonomous Robots Journal, Springer, 2015.
  • A. Wallar, E. Plaku, and D. Sofge, “Motion Planning for Surveillance of Risk-Sensitive Areas by a Team of Unmanned Aerial Vehicles,” IEEE Transactions on Automation Science and Engineering, Special Issue on Networked Cooperative Autonomy, IEEE, 2015.
  • L. Sabattini, F. Ehlers, and D. Sofge, “Guest Editorial,” IEEE Transactions on Automation Science and Engineering, Special Issue on Networked Cooperative Autonomy, IEEE Robotics and Automation Society, 2015.
  • N. Sydney and D. Sofge, “Distributed information-theoretic target detection using physics-inspired motion coordination,” Resilience Week (RWS), IEEE, 2015.
  • D.W. Yeo, N. Sydney, D. Paley, and D. Sofge, “Onboard Flow Sensing for Downwash Detection and Avoidance on Small Quadrotor Helicopters,” AIAA SciTech 2015, AIAA.
  • K. Sullivan, W. Lawson, and D. Sofge. "Improving Superpixel Boundaries Using Information Beyond the Visual Spectrum," IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015.
  • P. Elmore, E. Lawson, D. Smith, D. Sofge, and F. Petry, “Recognition of Seafloor Features by Decision Tree Algorithms in Scenes of Gridded Sonar Data,” 2014 Fall Meeting of the American Geophysical Union (AGU) Abstracts, American Geophysical Union, 2014.
  • K. Sullivan, W. Lawson, and D. Sofge, "Fusing laser reflectance and image data for terrain classification for small autonomous robots," 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, 2014.
  • M. Kuhlman, J. Hays, D. Sofge, and S.K. Gupta, “Central Pattern Generator Based Omnidirectional Locomotion for Quadrupedal Robotics,” Workshop on Real-time Motion Generation & Control, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2014.
  • T. Apker, S.Y. Liu, D. Sofge, and J.K. Hedrick, “Application of grazing-inspired guidance laws to autonomous information gathering,” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2014.
  • D. Sofge, N. Sydney, D. Paley, T. Apker, K. Sullivan, and M. Kuhlman, “Mobile Autonomous Navy Teams for Information Surveillance and Search (MANTISS),” Workshop on Crossing the Reality Gap: Control, Human Interaction and Cloud Technology for Multi- and Many-Robot Systems, 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2014.
  • F. Ehlers, D. Sofge, M. Chitre, and J. Potter, "Editorial: Distributed Mobile Sensor Networks for Hazardous Applications," International Journal of Distributed Sensor Networks, Special Issue on Distributed Mobile Sensor Networks for Hazardous Applications (F. Ehler, D. Sofge, M. Chitre, and J. Potter, Eds.), Hindawi Publishing, 2012.
  • D. Sofge and J. Whitman. "Long-range near-optimal path planning for gliders in complex high-energy environments." Autonomous Underwater Vehicles (AUV), 2010 IEEE/OES, IEEE, 2010.
  • D. Sofge, "Quantum Programming: Past, Present, Future." In Foundations of Probability and Physics—5. Vol. 1101. No. 1. AIP Publishing, 2009.
  • R. Wiegand, M. Potter, D. Sofge, and W. Spears, "A generalized graph-based method for engineering swarm solutions to multiagent problems," Parallel Problem Solving from Nature (PPSN) IX. Springer Berlin Heidelberg, 2006.
  • P. McDowell, B. Bourgeois, D. Sofge, and S. Iyengar, "Memory-based in situ learning for unmanned vehicles," Computer, 39, no. 12, IEEE, 2006.
  • D. Sofge, et al. "Collaborating with humanoid robots in space," International Journal of Humanoid Robotics, Vol. 02, Iss. 02, pp. 181-201, World Scientific, 2005.
  • D. Sofge, M. Potter, M. Bugajska, and A. Schultz, “Challenges and Opportunities of Evolutionary Robotics,” International Conference on Computational Intelligence, Robotics and Autonomous Systems, IEEE, 2003.
  • D. Sofge, "Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems," International Conference on Machine Learning and Applications (ICMLA’02), CSREA Press, 2002.
  • D. Sofge, K. De Jong, and A. Schultz "A blended population approach to cooperative coevolution for decomposition of complex problems," World Congress on Computational Intelligence (WCCI), IEEE, 2002.
  • D. Sofge and D.L. Elliott, “An Approach to Intelligent Identification and Control of Nonlinear Dynamical Systems,” Neural Adaptive Control Technology, World Scientific Series in Robotics and Intelligent Systems: Volume 15, R. Zbikowski and K.J. Hunt (Eds.), pp. 265-284, World Scientific, 1996.
  • D. Sofge, "Structural health monitoring using neural network based vibrational system identification," Second Australian and New Zealand Conference on Intelligent Information Systems, IEEE, 1994.
  • D. White and D. Sofge (Eds.), Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, Van Nostrand Reinhold Company, 1992.