David Braganza

A Neural Network Controller for Continuum Robots

Continuum or hyper-redundant robot manipulators can exhibit behavior similar to biological trunks, tentacles, or snakes. Unlike traditional rigid-link robot manipulators, continuum robot manipulators do not have rigid joints, hence these manipulators are extremely dexterous, compliant, and are capable of dynamic adaptive manipulation in unstructured environments. However, the development of high-performance control algorithms for these manipulators is quite a challenge, due to their unique design and the high degree of uncertainty in their dynamic models. In this paper, a controller for continuum robots, which utilizes a neural network feedforward component to compensate for dynamic uncertainties is presented. Experimental results using the OCTARM, which is a soft extensible continuum manipulator, are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved performance.

Tracking control for robot manipulators with kinematic and dynamic uncertainty

The control objective in many robot manipulator applications is to command the end-effector motion to achieve a desired response. To achieve this objective a mapping is required to relate the joint/link control inputs to the desired Cartesian position and orientation. If there are uncertainties or singularities in the mapping, then degraded performance or unpredictable responses by the manipulator are possible. To address these issues, an adaptive tracking controller is developed in this paper for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with three parameter representations are avoided.

An Adjustable Steer-by-Wire Haptic-Interface Tracking Controller for Ground Vehicles

The introduction of steer-by-wire system technology into ground transportation vehicles permits customization of the human-machine haptic interface to accommodate the drivers desired level of road ldquofeel.rdquo The ability to tune the steering systems dynamic behavior can potentially enhance the drivers overall performance and increase the vehicles safety. A nonlinear tracking controller is designed to ensure that the directional control steering assembly follows the operators commanded maneuvers at the driver interface. In addition, the controller provides the driver with tunable force feedback proportional to the reaction forces at the tire-road interface. Two control techniques are provided to guarantee that the corresponding tracking errors are asymptotically forced to zero. The first compensates for parametric uncertainty, whereas the second eliminates the need for torque measurements through the use of observers. Representative numerical and experimental results are presented to demonstrate the controllers performance for various driving scenarios.

Adaptive control of redundant robot manipulators with sub-task objectives*

In this paper, adaptive control of kinematically redundant robot manipulators is considered. An end-effector tracking controller is designed and the manipulators kinematic redundancy is utilized to integrate a general sub-task controller for self-motion control. The control objectives are achieved by designing a feedback linearizing controller that includes a least-squares estimation algorithm to compensate for the parametric uncertainties.

Adaptive control of redundant robot manipulators with sub-task objectives

In this paper, adaptive control of kinematically redundant robot manipulators is considered. An end-effector tracking controller is designed and the manipulators kinematic redundancy is utilized to integrate a general sub-task controller for self-motion control. The control objectives are achieved by designing a feedback linearizing controller that includes a least-squares estimation algorithm to compensate for the parametric uncertainties.

Tracking Control for Robot Manipulators with Kinematic and Dynamic Uncertainty

The control objective in many robot manipulator applications is to command the end-effector motion to achieve a desired response. To achieve this objective a mapping is required to relate the joint/link control inputs to the desired Cartesian position and orientation. If there are uncertainties or singularities in the mapping, then degraded performance or unpredictable responses by the manipulator are possible. To address these issues, an adaptive tracking controller is developed in this paper for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with three parameter representations are avoided.

Neural Network Grasping Controller for Continuum Robots

Continuum or hyper-redundant robots are robots which exhibit behavior similar to biological trunks, tentacles and snakes. Unlike traditional robots, continuum robot manipulators do not have rigid joints, hence the manipulators are compliant, extremely dexterous, and capable of dynamic, adaptive manipulation in unstructured environments; however, the development of high-performance control algorithms for these manipulators is a challenging problem. In this paper, we present an approach to whole arm grasping control for continuum robots. The grasping controller is developed in two stages; high level path planning for the grasping objective, and a low level joint controller using a neural network feedforward component to compensate for dynamic uncertainties. These techniques are used to enable whole arm grasping without using contact force measurements and without using a dynamic model of the continuum robot

Whole arm grasping control for redundant robot manipulators

An approach to whole arm grasping of objects using redundant robot manipulators is presented. A kinematic control which facilitates the encoding of both the end-effector position, as well as body self-motion positioning information as a desired trajectory signal for the manipulator joints is developed. A joint space controller which provides asymptotic tracking of the encoded desired trajectory in the presence of system uncertainties is presented

Euclidean position estimation of static features using a moving camera with known velocities

The estimation of 3D Euclidean coordinates of features from 2D images continues to be a problem of significant interest. In this paper we develop a 3D Euclidean position estimation strategy for a static object using a single moving camera whose motion is known. The Euclidean depth estimator which is developed has a very simple mathematical structure and is easy to implement. Numerical simulations are presented to illustrate the performance of the algorithm and an extension for a paracatadioptric system is briefly discussed.

Positioning of Large Surface Vessels using Multiple Tugboats

In this paper, the positioning of large surface vessels using multiple, autonomous tugboats is investigated. Specifically, the paper investigates the development of an adaptive position controller that compensates for select system parameters such as mass and drag coefficients and also for an unknown thrust configuration matrix which is the result of unknown tugboat locations. The control design is facilitated by strategic placement of the tugs about the vessel hull such that it does not require transmission of information between the tugs. Performance of the controller is investigated by simulations for a scaled model of a surface vessel.