Research: Robotics

Impact Dynamics

The control of dynamic systems that undergo an impact collision is both theoretically challenging and of practical importance. An appeal of studying systems that undergo an impact is that short-duration effects such as high stresses, rapid dissipation of energy, and fast acceleration and deceleration may be achieved from low-energy sources. However, colliding systems present a difficult control challenge because the equations of motion are different when the system state changes suddenly from a non-contact state to a contact state. In this research, the academic example of a planar robot colliding with an unactuated mass-spring system is used to represent a broader class of such systems. The control objective is to command a robot to collide with an unactuated mss-spring system, and regulate it to a desired compressed state while compensating for the unknown constant system parameters. Lyapunov-based methods are used to develop a continuous controller that ensures stable regulation of the mass and robot links. It is interesting to note that the same controller is used for the robot in free motion (i.e., decoupled from the mass-spring system), when the systems collide, and when the system dynamics are coupled. Our initial research on investigating robot impact with a stiff environment was motivated by industrial applications involving robots, such as material handling, painting, welding etc. More recently, the focus of the research has been to study robot interaction with a viscoelastic environment, with the goal of using robots for applications involving human-robot interaction (e.g., rehabilitation, surgery, soft robotic fingertips etc.).


Mobile Robots

This research is motivated by the desire to explore new control strategies for systems subject to nonholonomic motion constraints. Recent efforts have focused on the development of nonlinear, Lyapunov-based design and analysis tools to develop adaptive and robust tracking and regulation controllers for wheeled mobile robots, ships, and vertical take-off and landing (VTOL) systems.


Visual Servo Control

This research is motivated by the desire to enable autonomy in mechatronic systems for task execution in unstructured and dynamically changing environments. The research efforts exploit nonlinear, Lyapunov-based design and analysis techniques to develop visual servo control algorithms that enable vision-based feedback control despite disturbances such as uncertainty in the range to the target or uncertainty in the camera model (e.g., calibration, lens distortion).


Fault Detection and Identification

Several factors must be considered for robotic task execution in th presence of a fault, including: detection, identification, and accomodation for the fault. In this research work, a prediction error based dead-zone residual function and a nonlinear observer are used to detect and identify a class of actuator faults. Advantages of the proposed fault detection and identification methods are that they are based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller. A Lyapunov-based analysis is providd to prove that the developed fault observer converges to the actual fault. Simulation results are provided to illustrate the performance of the detection and identification methods.


Path Planning

Traditionally, robot control researchers have focused on the position tracking problem where the objective is to force the robot to follow a desired time dependent trajectory. Since the objective is encoded in terms of a time dependent trajectory, the robot may be forced to follow an unknown course to catch up with the desired trajectory in the presence of a large initial error. Motivated by task objectives that are more effectively described by on-line, state-dependent trajectories, two adaptive tracking controllers are developed that accommodate on-line path planning objectives.

  • Particle Filter based Path Planning