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Robotics, Automation and Autonomy

Robotics, Automation and Autonomy research is focused on fundamental, enabling science and engineering techniques related to robotics. Topics range from kinematics and mechanisms, dynamics and control, and intelligent materials to artificial intelligence. Areas of practical application of robotics and automation include macroscale/microscale manufacturing, mobile robots, assistive and prosthetic devices, soft robots, legged robotic systems, and unmanned aerial vehicles.

Research topics under this area include:

  1. Kinematics and dynamics of multi-body systems (Hereid, Siston, M. Srinivasan, Su) develops numerical methods and mathematical framework for kinematic analysis and dynamic simulations of complex articulated multi-body systems including but not limited to industrial serial and parallel manipulators, precision machinery, flexible continuum robots, human walking, biped and quadruped robots.
  2. Design of novel robotic systems (Hoelzle, Menq, Siston, M. Srinivasan, Su, Zhao) focuses on practical hardware implementation of new robotic systems including variable stiffness compliant mechanisms for human-safe corobots, transformable wheels for mobile robots, programmable soft robots, continuum robots, high precision nanopositioners, assistive devices, prostheses and exoskeletons, solar-powered unmanned aerial vehicles and networked UAV systems.
  3. System Optimization and control (Dai, Hereid, Hoelzle, Kumar, Menq, M. Srinivasan) creates numerical algorithms for modern optimization and control framework that integrates kinematic and dynamics simulation into algorithm objective functions. This includes the synthesis and design of optimal multi-body systems, motion plans of multi-body systems, or provably stable and robust control of the energetics and/or motions of multi-body systems.
  4. Autonomy (Aksun Guvenc, Dai, Gregory, Guvenc, Hereid, Hoelzle, Kumar, M. Srinivasan) focuses on intelligent machines that operate robustly under a variety of operating conditions with considerable uncertainties and without explicit human supervision, being perhaps capable of autonomously improving performance over time through learning and adaptation to the environment, potentially using modern AI and machine learning techniques.

 

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