Topics for Research in Robotics and Intelligent Systems

General areas for study and research:

Chemical and Biological Engineering

  • Control of chemical and biological dynamic processes
  • Optimal design of systems for material processing
  • Manipulation of matter at atomic and molecular scale

Civil and Environmental Engineering

  • Structural health monitoring and adaptive structures
  • Water resources
  • Earthquake detection and protective design
  • Remote sensing of natural resources
  • Urban planning and engineering

Computer Science

  • Theory and practice of computation for physical systems
  • Game playing, photo identification, and semantic identification
  • Real-time algorithms for measurement, prediction, and control
  • Artificial intelligence and machine learning
  • Databases, Internet security, and privacy

Electrical Engineering

  • Information theory
  • Electricity, microelectronics, and electromagnetism
  • Digital circuits and computation
  • Image processing, face, and character recognition
  • Video analysis and manipulation
  • Telecommunications networks
  • Autonomous vehicles

Mechanical and Aerospace Engineering

  • Robotic devices and systems
  • Autonomous air, sea, undersea, and land vehicles
  • Space exploration and development
  • Intelligent control systems
  • Biomimetic modeling, dynamics, and control
  • Cooperating robots for manufacturing and assembly
  • Cooperative control of natural and engineered groups
  • Identification of dynamic system models
  • Optimal state estimation and control

Operations Research and Financial Engineering

  • Intelligent transportation systems
  • Financial management and risk analysis
  • Dynamic resource management
  • Decision science
  • Optimal design

Philosophy

  • Knowledge, reasoning, and language
  • Logic and metaphysics
  • Politics and art of robotics and intelligent systems

Psychology

  • Inference, reasoning, problem solving
  • Human factors and human-machine interaction
  • Human motor control
  • Modeling perception
  • Neural network (connectionist) modeling of cognitive functions
  • Reinforcement learning
  • Study of brain function using functional magnetic response imaging, electrical, and optical methods