Research: Robotics
Robot Manipulation

Robot manipulators have widespread use in a variety of industries. Controlling a manipulator can be challenging since it is a multi-input multi-output system with a nonlinear response, where highly repeatable accuracy is required for most applications. Robot manipulators typically do not include sensors for full state feedback, motivating research efforts focused on output feedback control methods. Some robotic manipulators have hydraulic or cable driven actuators that result in additional dynamics that must be considered to yield desired performance demands. Joint friction and joint flexibility (e.g., due to series elastic actuators) also impact the dynamics. In some applications the robot may be tasked with imparting a force/torque on the environment. NCR research efforts have been motivated by and target solutions for each of these challenges. Key contributions include the development of a global adaptive output feedback controller, the development of novel continuously differentiable friction models (and associated controllers), compensation for rigid-link flexible-joint robots, and the development of robotic systems that interact with stiff (e.g., impact dynamics) or viscoelastic environments (e.g., needle insertion for robotic surgery).


Visual Servo Control

Unique control challenges exist when attempting to use image feedback in a closed-loop control system. For example, the image coordinates can be directly used as feedback (i.e., so-called image-based visual servo control), which provides a heuristic better chance of the features remaining in the field-of-view (FOV) since they are being regulated towards the center of the image. However, satisfying the desired feature trajectories can require large camera movements that are not physically realizable. Furthermore, the control laws can suffer from unpredictable singularities in the image Jacobian. Alternatively, the image coordinates can be related to a Euclidean coordinate system (i.e., so-called position-based visual servo control). Using the relative Euclidean coordinates typically yields physically valid camera trajectories, with known (and hence avoidable) singularities and no local minima. However, there is no explicit control of the image features, and features may leave the field of view, resulting in task failure. NCR efforts focus on the use of homography-based (or 2.5D) based approaches that combine the strengths of IBVS and PBVS to yield realizable control trajectories that also use image coordinates in the feedback. Such approaches have been used for develop image-based controllers to track desired image trajectories for various autonomous systems. A key innovation was the development of daisy-chaining as a means to relate current image features to a keyframe for visual odometry. Research efforts also focus on means to ensure features remain in the FOV during task execution, provide robustness when they leave the FOV, and path planning based on FOV constraints.


Fault Detection and Identification

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. Key NCR contributions include the development of a prediction error based dead-zone residual function and a nonlinear observer to detect and identify classes of actuator faults. An advantages of such fault detection and identification methods are that they are based on the nonlinear dynamic model of the robotic system (and can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller.


Mobile Robot

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


Sponsors

Ongoing Projects
AFRL: Privileged Sensing Framework


Completed Projects
Prioria: Observer Methods for Image Based Autonomous Navigation
Florida High Corridor Matching Funds
National Science Foundation (Award Number 0738091) SGER: Impact Modeling and Control for Human Robot Interaction
BARD: Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems
AFOSR: Research Institute for Autonomous Precision Guided Systems
DOE: University Research Program In Robotics For Environmental Restoration & Waste Management
IEEE: Robot Control Outreach Camps for Elementary and Middle School Children with Underrepresented Collegiate Camp Instructors
AFRL: Vision-Based Guidance and Control Algorithms Research
PRIORIA: Structure, Motion, and Geolocation Estimation for Autonomous Vehicles
ONR: Adaptive Dynamic Programming for Autonomous Underwater Vehicles
National Science Foundation (Award Number 1217908) NeTS: Small: Network Connectivity and Security for Cooperative Autonomous Vehicles
DOA Natl. Inst. of Food & Ag.: NRI: An Integrated Machine Vision-based Control for Citrus Fruit Harvesting Using Enhanced 3D Mapping, Path Planning and Servo Control
AFRL: AFRL Mathematical Modeling and Optimization Institute
PRIORIA: CL-Based Image Estimation

Related NCR Robotics Publications