Univ., Diss--Wuppertal. / Literaturverz. S. 120 - 124. Nebensacht.: Manipulator control by neural networks.

Ilmenau, Techn. University, Diss., 2002.

Essen, Universiẗat, Diss., 2003.

Duisburg, Essen, University, Diss., 2004.

Graduate

Approved for public release; distribution is unlimited / Today's demand for a high speed, low weight and large load capable manipulator has spurred the research on flexible manipulators. This thesis centers on an implementation of dynamic control on a single-link flexible arm utilizing a general purpose micro-computer. This research also studies the dynamic behavior of the control system with a brief comparison of the derived flexible-body-model controller to a rigid-body-model controller. / http://archive.org/details/implementationof00kirk / Lieutenant, United States Navy

This dissertation summarizes the research effort to develop a novel, three degree-of-freedom device that is ideally suited as a robotic wrist or platform manipulator. Because of its similarity to the human wrist, this invention has been named the "Carpal Wrist." Much like its natural counterpart, the Carpal Wrist has eight primary links, corresponding to the eight carpal bones of the human wrist, a parallel actuation scheme, similar to the flexor and extensor carpi muscles along the forearm, and an open interior passage, which forms a protected tunnel for routing hoses and electrical cables, much like the well-known carpal tunnel. The Carpal Wrist also has the significant advantages of possessing closed-form forward and inverse kinematic solutions and a large, dexterous workspace that is free of interior singularities (either considered separately or as part of a manipulator arm). As a result of its symmetric parallel architecture, the Wrist can handle a large payload capacity and can easily be adapted to a variety of actuation schemes. While parallel-architecture manipulators have long been recognized for their high-rigidity and large payload-to-weight capacity, few have been developed for application, primarily because of complications in kinematic and dynamic modeling. The mathematical model of any manipulator must be developed in order to allow the necessary motion control of the device. The mathematical model provides a mapping from the input space (called joint space) to the output space (called tool space) of the manipulator. Given a desired task in terms of motion of the robot tool, the mathematical model determines the required motor input parameters. Advanced manipulator performance through automatic control becomes possible when the model includes inertial or dynamic effects of the manipulator and tool. The research leading to the development of the Carpal Wrist is significant because it presents a complete kinematic and dynamic model of a parallel-architecture manipulator, and thus will provide significant improvement over current serial robot technology. This research was funded in part by TRIAD Investors Corporation (University Partners), Baltimore MD. / Ph. D.

<p>A kinematically redundant four degrees of freedommanipulator arm, a knuckle boom, is studied. Three joints arerevolute and one linear. Since only three degrees of freedomare needed for positioning, we have one redundant degree offreedom. Three different kinematic control strategies arestudied. One is based on the maximization of velocity (localoptimisation). This strategy is non-repeatable and cansometimes lead to kinematically unfavourable positions. In thesecond strategy, which is based on the maximization of staticlifting capacity (local optimisation), one of the degrees offreedom (the linear joint) is made a function of the toolcentre point position. The third strategy, which is based ondynamic programming (global optimization), calculates theshortest time that is possible for the tool centre point to gofrom one point to another point in the working area. The threestrategies are compared in a simulation study. The simulationsshow the necessary speed requirements for all joints whenperforming straight paths in the manipulator work area. Thesimulations also show the difference in time consumptionsbetween the three control algorithms and also what happens whenthe joints reach their maximum velocity limits.</p><p><strong>Keywords</strong>Manipulator, Kinematic Control, RedundantLanguage</p>

A mobile manipulator is a robotic system consisting of a robotic manipulator mounted onto a mobile base. This greatly extends the workspace of the robotic manipulator and allows it to perform more tasks. However, combining both systems increases the complexity of the control task as well as introducing additional controller tasks such as coordination of motion, where executing the task can involve using both the mobile base and manipulator, and cooperation of task, where many tasks can be executed at once. In this thesis a controller for a mobile manipulator is developed from smaller, simple controller blocks, allowing the controller to be flexible, easy to understand, and straightforward to implement using well-known embedded software implementation approaches. A behaviour-based approach was used to build the individual controllers, and a hierarchical structure was used to organise the individual controllers to provide cooperation between them and coordinated motion. The task assigned to the controller was to reach a series of waypoints in a large workspace, while satisfying performance metrics of manipulability and tip-over sta- bility. The operation of the controller was tested in simulation using 100 randomly generated scenarios consisting of five randomly generated waypoints in each. Using default thresholds for manipulability and tip-over stability, the controller was success- fully able to complete all scenarios. Further simulations were then performed testing the effects of varying the thresholds of the performance metrics to explore the tradeoffs involved in different parameter choices. The controller was successful in a majority of these scenarios, with only a few failing due to extreme threshold choices. The reasons for these failures, and the corresponding lessons for robot designers are discussed. Finally, to demonstrate the modularity of the controller, an obstacle avoidance con- troller was added and simulation results showed the controller was capable of avoiding obstacles while still performing the same tasks that were used in previous tests. Successful simulation results of the controller across a range of performance metrics shows that the combination of a behaviour based and hierarchical approach to mobile manipulator control is not only capable of producing a functional controller, but also one that is more modular and easier to understand than the monolithic controllers developed by other researchers.

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