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Laura Ray headshot

Laura Ray

Professor of Engineering

Senior Associate Dean, Faculty Development

Laura Ray autonomous robot


  • BSE, Mechanical and Aerospace Engineering, Princeton University 1984
  • MSE, Mechanical Engineering, Stanford University 1985
  • PhD, Mechanical and Aerospace Engineering, Princeton University 1991

Research Interests

System dynamics and controls; robotics; signal processing; machine intelligence

Selected Publications

  • Jerald D. Kralik, Tao Mao, Zhao Cheng, and Laura E. Ray, Modeling Incubation and Restructuring for Robotic Creative Problem Solving, Journal of Robotics and Autonomous Systems, special issue on Robotics and Creativity, 86, 1–12, Dec 2016.
  • S.A. Arcone, J.H. Lever, J., L. Ray, G. Hamilton, B. Walker* P. Koons, Ground-Penetrating Radar Profiles of the McMurdo Shear Zone, Antarctica Acquired with an Unmanned Rover: Interpretation of Crevasses, Fractures and Folds within Firn and Marine Ice, Geophysics, 81(1), Jan 2016.
  • K. Fink and L. Ray, Individualization of Head Related Transfer Functions using Principal Component Analysis, Applied Acoustics, 87 (2015) 162–173.
  • R. Williams, L. Ray, J. Lever, A. Burzynski, Ground Penetrating Radar Data Classification using Hidden Markov Models and Support Vector Machines, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 2014, 4836 – 4848.
  • J.H. Lever, A.J. Delaney, L.E Ray, E. Trautmann, L.A. Barna, A.M Burzynski. Autonomous GPR Surveys using Polar Rover Yeti, Journal of Field Robotics, 2012, DOI: 10.1002/rob.21445.
  • T. Mao and L. Ray, Hierarchical State Representation and Action Abstractions in Q-Learning for Agent-Based Herding, International Journal of Information and Electronics Engineering, 2(4), 538–542, 2012.
  • E. Trautmann and L. Ray, Mobility Characterization for Autonomous Mobile Robots using Machine Learning, Autonomous Robots, 30: 369–383, 2011, DOI 10.1007/s10514-011-9224-5.


  • American Society for Non-destructive Testing Fellowship (2001)
  • SAE Ralph R. Teetor Educational Award (1997)

Professional Activities

  • American Society of Mechanical Engineers member
  • Institute of Electrical and Electronics Engineers member



  • System and method for identifying ictal states in a patient | 10,485,471
  • Method for tuning an adaptive leaky LMS filter | 6,741,707
  • Tuned feedforward LMS filter with feedback control | 6,996,241
  • In-ear digital electronic noise cancelling and communication device | 8,385,560

Research Projects

  • Cooperative control of multi-robot systems

    Cooperative control of multi-robot systems

    Cooperative control of multi-robot systems focuses on modeling and control of groups of high-speed mobile robots while accommodating communication latencies and nonlinear vehicle dynamics. In distributed cooperative control, robots communicate information about their state to each other; communication latencies and error depends on the amount of information communicated and the number of robots. We are developing distributed control system modeling and design tools that seek to maximize control bandwidth for a given information set. These tools will also assist in assessing the value of information transmitted in maintaining stability and performance of group dynamics. Both potential function path planning and control and predictive control methods are being developed.

  • Terrain identification

    Terrain identification

    Terrain identification research focuses on using small, lightweight robots to classify, characterize, and identify terrain properties necessary to predict mobility of these vehicles on the terrain. Terramechanics models for heavy vehicles are well understood, but similar comprehensive models do not exist for lightweight (sub-500 kg) vehicles. We are developing terrain models and modeling tools that can be used to asses mobility on a given terrain, while avoiding maneuvers that cause immobilization. We seek to integrate terrain identification and traction/stability control of the robots in order to allow autonomous or remote control of these robots at the maximum attainable speeds and accelerations achievable on the terrain.

  • Robot design and smart navigation

    Robot design and smart navigation

    Robot design and smart navigation focuses on developing affordable robot designs that employ "smart navigation" for path planning and mobility in extreme terrain, rather than complex and expensive vision systems. We are developing solar-powered robotic platforms for deploying scientific instrumentation over hundreds of kilometers in Arctic and Antarctic regions. These robots employ proprioceptive sensors to determine whether difficult terrain is passable, and if not, to navigate around such terrain.

  • Acoustics and signal processing

    Acoustics and signal processing

    Acoustics and signal processing research focuses on active noise control and distributed sensing. Active noise control reduces noise in hearing protection and communication systems to reduce noise induced hearing loss and to enhance the ability to communicate. Distributed sensing research uses signal processing to focus listening in a specified direction. This research blends mechatronics—the design of mechanical and electrical systems—with high performance signal processing and control algorithms to improve communication in noisy environments.


A Decade of Challenges and Progress for Polar Robots

Graduate Student Engineering Research: Polar Ice Research - Cool Robot

Graduate Student Engineering Research: Yeti Robot

Graduate Student Engineering Research: Psychoacoustics

Cooperative Robots

Professor Laura Ray on the Luce Graduate Fellowships

Yeti Robot with Tucker Sno-Cat

Dartmouth PhD Innovation Program: Harrison Hall


Business decisions
Jun 12, 2015
Cold hard facts
Jan 31, 2012
Hear, hear!
Mar 07, 2011
A Sound Solution
Mar 07, 2011