Canonical solution groups of a homomorphism : manipulator kinematics, nonparametric regression and distributed object systems

The kinematic mapping of a rigid open-link manipulator is a homomorphism between Lie groups. The homomorphisrn has solution groups that act on an inverse kinematic solution element. A canonical representation of solution group operators that act on a solution element of three and seven degree-of-freedom (do!) dextrous manipulators is determined by geometric analysis. Seven canonical solution groups are determined for the seven do! Robotics Research K-1207 and Hollerbach arms. The solution element of a dextrous manipulator is a collection of trivial fibre bundles with solution fibres homotopic to the Torus. If fibre solutions are parameterised by a scalar, a direct inverse funct.ion that maps the scalar and Cartesian base space coordinates to solution element fibre coordinates may be defined. A direct inverse pararneterisation of a solution element may be approximated by a local linear map generated by an inverse augmented Jacobian correction of a linear interpolation. The action of canonical solution group operators on a local linear approximation of the solution element of inverse kinematics of dextrous manipulators generates cyclical solutions. The solution representation is proposed as a model of inverse kinematic transformations in primate nervous systems. Simultaneous calibration of a composition of stereo-camera and manipulator kinematic models is under-determined by equi-output parameter groups in the composition of stereo-camera and Denavit Hartenberg (DH) rnodels. An error measure for simultaneous calibration of a composition of models is derived and parameter subsets with no equi-output groups are determined by numerical experiments to simultaneously calibrate the composition of homogeneous or pan-tilt stereo-camera with DH models. For acceleration of exact Newton second-order re-calibration of DH parameters after a sequential calibration of stereo-camera and DH parameters, an optimal numerical evaluation of DH matrix first order and second order error derivatives with respect to a re-calibration error function is derived, implemented and tested. A distributed object environment for point and click image-based tele-command of manipulators and stereo-cameras is specified and implemented that supports rapid prototyping of numerical experiments in distributed system control. The environment is validated by a hierarchical k-fold cross validated calibration to Cartesian space of a radial basis function regression correction of an affine stereo model. Basic design and performance requirements are defined for scalable virtual micro-kernels that broker inter-Java-virtual-machine remote method invocations between components of secure manageable fault-tolerant open distributed agile Total Quality Managed ISO 9000+ conformant Just in Time manufacturing systems.

The application of robotics to the turbine blade encapsulation process

A survey of the existing state-of-the-art of turbine blade manufacture highlights two operations that have not been automated namely that of loading of a turbine blade into an encapsulation die, and that of removing a machined blade from the encapsulation block. The automation of blade decapsulation has not been pursued. In order to develop a system to automate the loading of an encapsulation die a prototype mechanical handling robot has been designed together with a computer controlled encapsulation die. The robot has been designed as a mechanical handling robot of cylindrical geometry, suitable for use in a circular work cell. It is the prototype for a production model to be called `The Cybermate'. The prototype robot is mechanically complete but due to unforeseen circumstances the robot control system is not available (the development of the control system did not form a part of this project), hence it has not been possible to fully test and assess the robot mechanical design. Robot loading of the encapsulation die has thus been simulated. The research work with regard to the encapsulation die has focused on the development of computer controlled, hydraulically actuated, location pins. Such pins compensate for the inherent positional inaccuracy of the loading robot and reproduce the dexterity of the human operator. Each pin comprises a miniature hydraulic cylinder, controlled by a standard bidirectional flow control valve. The precision positional control is obtained through pulsing of the valves under software control, with positional feedback from an 8-bit transducer. A test-rig comprising one hydraulic location pin together with an opposing spring loaded pin has demonstrated that such a pin arrangement can be controlled with a repeatability of +/-.00045'. In addition this test-rig has demonstrated that such a pin arrangement can be used to gauge and compensate for the dimensional error of the component held between the pins, by offsetting the pin datum positions to allow for the component error. A gauging repeatability of +/- 0.00015' was demonstrated. This work has led to the design and manufacture of an encapsulation die comprising ten such pins and the associated computer software. All aspects of the control software except blade gauging and positional data storage have been demonstrated. Work is now required to achieve the accuracy of control demonstrated by the single pin test-rig, with each of the ten pins in the encapsulation die. This would allow trials of the complete loading cycle to take place.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.