Self Organising Maps for Anatomical Joint Constraint

The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate ingularities when modelling orientations between limbs at a joint. This has led to the development of quaternion based joint constraint validation and correction methods. In this paper a novel method for implicitly modelling unit quaternion joint constraints using Self Organizing Maps (SOMs) is proposed which attempts to address the limitations of current constraint validation and correction approaches. Initial results show that the resulting SOMs are capable of modelling regular spherical constraints on the orientation of the limb

A high speed scanning system for vision based navigation/control of mobile robots.

One of the main problems in the design of mobile robots is the development of creating smart integrated information systems. These systems may include different type of sensors. Usually a CeD vision system is a necessary part for these systems. This thesis considers the design of a fast mechanical scanning system for a CCD vision system and the synthesis of the optimal control for this system. The mathematical model of the transport subsystem for mobile robots subjected to an external disturbance is created. The correctness of this model is proved on the base of simulation and experimental results. Regression coefficients are calculated and an estimate of model accuracy is carried out. The approach of power calculation for the actuators for the fast mechanical scanning system is considered. Implementation of this approach extends the use of a general mathematical model of the transport subsystem of the mobile robot and as such considerably reduces the design time. The main features for estimation of external disturbances are determined. Limitations of implementation of some technical solutions for mobile robot sensors are defined according to an analysis conducted of different factors for external disturbances. Di~erent kinematic schemes of the scanning systems have been analysed in this thesis. Practical recommendations of the kinematic scheme used in scanning systems are given. The essential features of a kinematic scheme for the fast mechanical scanning system have been developed and verified. A method for the solution of the inverse kinematics of a 3 degree of freedom scanning system in terms of velocities and in accelerations is presented. This method is utilised for formulating optimal control for the fast mechanical scanning system. 11 v ASlLY RUBrsov PHD· THESIS The algorithms of fast scanning have been produced for the different types of the sensors. The limitations for the practical realisations for these algorithms are considered in this thesis. The optimal control algorithms for the developed scanning system are produced. This control minimises the sum of instant powers of the scanning system actuators. A practical algorithm has been derived utilising the control scheme structure developed theoretically. The capability of this control algorithm has been proved by experimental study. Advantages of this developed control algorithm for 3 degree scanning system has been proven by experimentation

Motion/Force transmission analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains

 This thesis presents several results regarding the kinematic performance analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains. Screw theory based methods have been utilised to generate new indices, along with a formal procedure, enabling the systematic and complete singularity and motion/force transmission analysis of parallel mechanisms with these closed-loop sub-chains

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

Graphical modelling of modular machines

This research is aimed at advancing machine design through specifying and implementing (in "proof of concept" form) a set of tools which graphically model modular machines. The tools allow mechanical building elements (or machine modules) to be selected and configured together in a highly flexible manner so that operation of the chosen configuration can be simulated and performance properties evaluated. Implementation of the tools has involved an extension in capability of a proprietary robot simulation system. This research has resulted in a general approach to graphically modelling manufacturing machines built from modular elements. A focus of study has been on a decomposition of machine functionality leading to the establishment of a library of modular machine primitives. This provides a useful source of commonly required machine building elements for use by machine designers. Study has also focussed on the generation of machine configuration tools which facilitate the construction of a simulation model and ultimately the physical machine itself. Simulation aspects of machine control are also considered which depict methods of manipulating a machine model in the simulation phase. In addition methods of achieving machine programming have been considered which specify the machine and its operational tasks. Means of adopting common information data structures are also considered which can facilitate interfacing with other systems, including the physical machine system constructed as an issue of the simulation phase. Each of these study areas is addressed in its own context, but collectively they provide a means of creating a complete modular machine design environment which can provide significant assistance to machine designers. Part of the methodology employed in the study is based on the use of the discrete event simulation technique. To easily and effectively describe a modular machine and its activity in a simulation model, a hierarchical ring and tree data structure has been designed and implemented. The modularity and reconfigurability are accommodated by the data structure, and homogeneous transformations are adopted to determine the spatial location and orientation of each of the machine elements. A three-level machine task programming approach is used to describe the machine's activities. A common data format method is used to interface the machine design environment with the physical machine and other building blocks of manufacturing systems (such as CAD systems) where systems integration approaches can lead to enhanced product realisation. The study concludes that a modular machine design environment can be created by employing the graphical simulation approach together with a set of comprehensive configuration. tools. A generic framework has been derived which outlines the way in which machine design environments can be constructed and suggestions are made as to how the proof of concept design environment implemented in this study can be advanced

Dual drive series actuator

Industrial robotic manipulators can be found in most factories today. Their tasks are accomplished through actively moving, placing and assembling parts. This movement is facilitated by actuators that apply a torque in response to a command signal. The presence of friction and possibly backlash have instigated the development of sophisticated compensation and control methods in order to achieve the desired performance may that be accurate motion tracking, fast movement or in fact contact with the environment. This thesis presents a dual drive actuator design that is capable of physically linearising friction and hence eliminating the need for complex compensation algorithms. A number of mathematical models are derived that allow for the simulation of the actuator dynamics. The actuator may be constructed using geared dc motors, in which case the benefits of torque magnification is retained whilst the increased non-linear friction effects are also linearised. An additional benefit of the actuator is the high quality, low latency output position signal provided by the differencing of the two drive positions. Due to this and the linearised nature of friction, the actuator is well suited for low velocity, stop-start applications, micro-manipulation and even in hard-contact tasks. There are, however, disadvantages to its design. When idle, the device uses power whilst many other, single drive actuators do not. Also the complexity of the models mean that parameterisation is difficult. Management of start-up conditions still pose a challenge

Computing global configuration-space maps using multidimensional set-theoretic modelling

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Learning forward-models for robot manipulation

Robots with the capability to dexterously manipulate objects have the potential to revolutionise automation. Interestingly, human beings have the ability to perform complex object manipulations. They perfect their skills to manipulate objects through repeated trials. There is extensive human motor control literature which provides evidence that the repetition of a task creates forward-models of that task in the brain. These forward-models are used to predict future states of the task, anticipate necessary control actions and adapt impedance quickly to match task requirements. Evidence from motor control and some promising results in the robot research on manipulation clearly shows the need for forward-models for manipulation. This study was started with the premise that a robot needs forward-models to perform dexterous manipulation. Initially planning a sequence of actions using a forward-model was identified as the most crucial problem in manipulation. Push manipulation planning using forward-models was the first step in this direction. However, unlike most methods in the robotic push manipulation literature, the approach was to incorporate the uncertainty of the forward-model in formulating the plan for push planning. Incorporating uncertainty helps the robot to perform risk-aware actions and stay close to the known areas of the state space while manipulating the object. The forward-models of object dynamics were learned offline, and robot pushes were fixed-duration position-controlled actions. The experiments in simulation and real robots were successful and helped in creating several other insights for better manipulation. Two of these insights were the need to have the capability to learn the feed-forward model online and the importance of having a state-dependent stiffness controller. The first part of the thesis presents a planner that makes use of an uncertain, learned, forward (dynamical) model to plan push manipulation. The forward-model of the system is learned by poking the object in random directions. The learned model is then utilised by a model predictive path integral controller to push the box to the required goal pose. By using path-integral control, the proposed planner can find efficient paths by sampling. The planner is agnostic to the forward-model used and produces successful results using a physics simulator, an Ensemble of Mixture Density Networks (Ensemble-MDN) or a Gaussian Process (GP). Both ensemble-MDN and a GP can encode uncertainty not only in the push outcome but in the model itself. The work compares planning using each of these learned models to planning with a physics simulator. Two versions of the planner are implemented. The first version makes uncertainty averse push actions by minimising uncertainty cost and goal costs together. Using multiple costs makes it difficult for the optimiser to find optimal push actions. Hence the second version solves the problem in two stages. The first stage creates an uncertainty averse path, and the second stage finds push actions to follow the path found. The second part of the thesis describes a framework which can learn forward-models online and can perform state-dependent stiffness adaptation using these forward-models. The idea of the framework is again motivated by the human control literature. During the initial trials of a novel manipulation task, humans tend to keep their arms stiff to reduce the effects of any unforeseen disturbances on the ability to perform the task accurately. After a few repetitions, humans adapt the stiffness of their arms without any significant reduction in task performance. Research in human motor control strongly indicates that humans learn and continuously revise internal models of manipulation tasks to support such adaptive behaviour. Drawing inspiration from these findings, the proposed framework supports online learning of a time-independent forward-model of a manipulation task from a small number of examples. The proposed framework consists of two parts. The first part can create forward-models of a task through online learning. Later, the measured inaccuracies in the predictions of this model are used to dynamically update the forward-model and modify the impedance parameters of a feedback controller during task execution. Furthermore, the framework includes a hybrid force-motion controller that enables the robot to be compliant in particular directions (if required) while adapting the impedance in other directions. These capabilities are illustrated and evaluated on continuous contact tasks such as polishing a board, pulling a non-linear spring and stirring porridge