Title: IVA Movement Quality Improvement for the Virtual Environment of Unreal Tournament 2004 Author: Bohuslav Macháč Department / Institute: Department of Software and Computer Science Education Supervisor of the master thesis: Mgr. Jakub Gemrot, Department of Software and Computer Science Education Abstract: PogamutUT2004 is an extension of the Pogamut platform designed for developing intelligent virtual agents (IVAs) in Unreal Tournament 2004. Navigation of IVAs in Pogamut is handled by a navigation system, which uses a navigation graph as an environment abstraction. Navigation mesh is a new, more advanced abstraction, but the existing navigation system is not capable of using its advantages. We created a new navigation system, which exploits advantages of the navigation mesh and solves several other issues of the old one. We show that the new navigation system improves the quality of navigation. To demonstrate the quality improvement, an evaluation framework was created for the comparison of navigation systems. Systems were compared in terms of total number of significant paths on the map, which the system is able to follow, length of the path and time of the navigation. We selected 18 different maps for thorough evaluation and we performed the basic evaluation on 58 other maps. The new system is more...

We propose and solve a synchronized path following problem for a differential drive robot modeled as a dynamic unicycle and an Euler-Lagrange system. Each system is assigned a simple closed curve in its output space. The outputs of systems must approach and traverse their assigned curves while synchronizing their motions along the paths. The synchronization problems we study in this thesis include velocity synchronization and position synchronization. Velocity synchronization aims to force the velocities of the systems be equal on the desired paths. Position synchronization entails enforcing a positional constraint between the systems modeled as a constraint function on the paths. After characterizing feasible positional constraints, a finite-time stabilizing control law is used to enforce the position constraint.

In this thesis we investigate the problem of designing a control system for a modern bicycle so that the bicycle is stable and follows a path. We propose a multi-loop control architecture, where each loop is systematically designed using linear control techniques. The proposed strategy guarantees that the bicycle asymptotically converges to paths of constant curvature. A key advantage of our approach is that by using linear techniques analysis and controller design are relatively simple. We base our control design on the nonlinear (corrected) Whipple model, which has been previously verified for correctness and experimentally validated. The equations of motion for the nonlinear model are very complicated, and would take many pages to explicitly state. They also have no known closed form solution. To enable analysis of the model we linearize it about a trajectory such that the bicycle is upright and travelling straight ahead. This linearization allows us to arrive at a parameterized linear time-invariant state-space representation of the bicycle dynamics, suitable for analysis and control design. The inner-loop control consists of a forward-speed controller as well as a lean and steer controller. To keep the bicycle at a constant forward speed, we develop a high-bandwidth proportional controller that uses a torque along the axis of the rear wheel of the bicycle to keep the angular velocity of the rear wheel at a constant setpoint. To stabilize the bicycle at this forward speed, lean torque and steer torque are treated as the control signals. We design a state-feedback controller and augment integrators to the output feedback of the lean angle and steer angle to provide perfect steady-state tracking. To arrive at the gains for state feedback, linear-quadratic regulator methods are used. When following a constant-curvature path, a vehicle has a constant yaw rate. Using this knowledge, we begin designing the outer-loop path-following control by finding a map that converts a yaw rate into appropriate lean angle and steer angle references for the inner loop. After the map is completed, system identification is performed by applying a yaw-rate reference to the map and analyzing the response of the bicycle. Using the linear approximation obtained, a classical feedback controller for yaw-rate tracking is designed. In addition to yaw-rate control, to track a path the yaw angle of the bicycle must match that of the path and the bicycle must physically be on the path. To analyze these conditions a linear approximation for the distance between the bicycle to the path is found, enabling construction of a linear approximation of the entire system. We then find that by passing the signal for the difference in yaw rate and the distance through separate controllers, summing their output, and subtracting from the reference yaw rate of the path, the bicycle converges to the path. After developing the general design procedure, the final part of the thesis shows a step by step design example and demonstrates the results of applying the proposed control architecture to the nonlinear bicycle model. We highlight some problems that can arise when the bicycle is started far from the path. To overcome these problems we develop the concept of a virtual path, which is a path that when followed returns the bicycle to the actual path. We also recognize that, in practice, typical paths do not have constant curvature, so we construct more practical paths by joining straight line segments and circular arc segments, representing a practical path similar to a path that would be encountered when biking through a series of rural roads. Finally, we finish the design example by demonstrating the performance of the control architecture on such a path. From these simulations we show that using the suggested controller design that the bicycle will converge to a constant curvature path. Additionally with using the controllers we develop that in the absence of disturbance the bicycle will stay within the intended traffic lane when travelling on a typical rural road.

We propose and solve a synchronized path following problem for a differential drive robot modeled as a dynamic unicycle and an Euler-Lagrange system. Each system is assigned a simple closed curve in its output space. The outputs of systems must approach and traverse their assigned curves while synchronizing their motions along the paths. The synchronization problems we study in this thesis include velocity synchronization and position synchronization. Velocity synchronization aims to force the velocities of the systems be equal on the desired paths. Position synchronization entails enforcing a positional constraint between the systems modeled as a constraint function on the paths. After characterizing feasible positional constraints, a finite-time stabilizing control law is used to enforce the position constraint.

In this thesis we investigate the problem of designing a control system for a modern bicycle so that the bicycle is stable and follows a path. We propose a multi-loop control architecture, where each loop is systematically designed using linear control techniques. The proposed strategy guarantees that the bicycle asymptotically converges to paths of constant curvature. A key advantage of our approach is that by using linear techniques analysis and controller design are relatively simple. We base our control design on the nonlinear (corrected) Whipple model, which has been previously verified for correctness and experimentally validated. The equations of motion for the nonlinear model are very complicated, and would take many pages to explicitly state. They also have no known closed form solution. To enable analysis of the model we linearize it about a trajectory such that the bicycle is upright and travelling straight ahead. This linearization allows us to arrive at a parameterized linear time-invariant state-space representation of the bicycle dynamics, suitable for analysis and control design. The inner-loop control consists of a forward-speed controller as well as a lean and steer controller. To keep the bicycle at a constant forward speed, we develop a high-bandwidth proportional controller that uses a torque along the axis of the rear wheel of the bicycle to keep the angular velocity of the rear wheel at a constant setpoint. To stabilize the bicycle at this forward speed, lean torque and steer torque are treated as the control signals. We design a state-feedback controller and augment integrators to the output feedback of the lean angle and steer angle to provide perfect steady-state tracking. To arrive at the gains for state feedback, linear-quadratic regulator methods are used. When following a constant-curvature path, a vehicle has a constant yaw rate. Using this knowledge, we begin designing the outer-loop path-following control by finding a map that converts a yaw rate into appropriate lean angle and steer angle references for the inner loop. After the map is completed, system identification is performed by applying a yaw-rate reference to the map and analyzing the response of the bicycle. Using the linear approximation obtained, a classical feedback controller for yaw-rate tracking is designed. In addition to yaw-rate control, to track a path the yaw angle of the bicycle must match that of the path and the bicycle must physically be on the path. To analyze these conditions a linear approximation for the distance between the bicycle to the path is found, enabling construction of a linear approximation of the entire system. We then find that by passing the signal for the difference in yaw rate and the distance through separate controllers, summing their output, and subtracting from the reference yaw rate of the path, the bicycle converges to the path. After developing the general design procedure, the final part of the thesis shows a step by step design example and demonstrates the results of applying the proposed control architecture to the nonlinear bicycle model. We highlight some problems that can arise when the bicycle is started far from the path. To overcome these problems we develop the concept of a virtual path, which is a path that when followed returns the bicycle to the actual path. We also recognize that, in practice, typical paths do not have constant curvature, so we construct more practical paths by joining straight line segments and circular arc segments, representing a practical path similar to a path that would be encountered when biking through a series of rural roads. Finally, we finish the design example by demonstrating the performance of the control architecture on such a path. From these simulations we show that using the suggested controller design that the bicycle will converge to a constant curvature path. Additionally with using the controllers we develop that in the absence of disturbance the bicycle will stay within the intended traffic lane when travelling on a typical rural road.

Cooperative control for miniature air vehicles (MAVs) is currently a highly researched topic. There are many application for which MAVs are well suited, including fire monitoring, surveillance and reconaissance, and search and rescue missions. All of these applications can be carried out more effictively by a team of MAVs than by a single vehicle. As technologies for microcontrollers and small sensors have improved so have the capabilities of MAVs. This improvement in MAV performance abilities increases the possibility for cooperative missions. The focus of this research was on cooperative timing missions. The issues faced when dealing with multi-MAV flight include information transfer, real time path planning, and maintenance of a fleet of flight-worthy MAVs. Additional challenges associated with timing missions include path following and velocity control. Two timing scenarios were studied and both of these scenarios were flight tested. The first scenario was a sequenced arrival of the MAVs over a target at a predetermined fly-through heading. The second scenario was a simultaneous arrival of the team ofMAVs over a known target location. The ideas of coordination functions and coordination variables have been employed to achieve coordination. Experimental results verify the feasibility of real time coperative control for a team of MAVs. Initial cooperative timing tests revealed the need for more accurate path following. Accordingly, a new method for path following using vector fields was developed. A vector field of desired ground track headings is calculated and commanded ground track headings are calculated such that ground track heading error and lateral following error decay asymptotically even in the presence of constant wind disturbances. The utilization of ground track heading and ground speed in the path following control, in combination with the vector field methods is what makes this zero-error following possible. Methods for following straight lines and orbits as well as combinations of the lines and circular arcs are presented. The assertions that minimal following errors result when using these methods have been verified experimentally.

No description available.

Increased safety and productivity are driving factors for the trend in the mining industry where equipment and machines increasingly get automated. An example is the load-haul-dump vehicle, which is a machine that is used for transport of ore in underground mines. The cyclic load-haul-dump process is well suited for automation and automated loaders are commercially available today. Recent advances in autonomous driving have raised questions if there are efficiency gains that can be made by improving the path following algorithms that are used in the control. The aim of this thesis is to investigate the usage of model predictive control for path following for center-articulated mining vehicles. Two path following nonlinear model predictive controllers are designed and implemented. One controller is based on an error dynamics model, formulated as a regulation problem and implemented with the open source NMPC-library GRAMPC. The second controller is based on a kinematic model, formulated as a reference tracking NMPC problem and implemented using the embedded-MPC software tool FORCESPRO. The controllers are simulated on the same hardware that is used in real load-haul-dump vehicles, in a simulation environment provided by Epiroc Rock Drills AB. The results from the simulations show that both controllers can successfully follow a path, with a similar level of path error and less aggressive control actions compared to the current path following controller. The implemented controllers perform the control computations within a range of milliseconds on the embedded hardware, which is fast enough for real-time operation of the load-haul-dump vehicle.

The main contributions of this work are applications of robust control and analysis methods to complex engineering systems, namely, small fixed-wing unmanned aircraft systems (UAS). Multiple path-following controllers for a small fixed-wing Telemaster UAS are presented, including a linear parameter-varying (LPV) controller scheduled over path curvature. The controllers are synthesized based on a lumped path-following and UAS dynamic system, effectively combining the six degree-of-freedom aircraft dynamics with established parallel transport frame virtual vehicle dynamics. The robustness and performance of these controllers are tested in a rigorous MATLAB simulation environment that includes steady winds, turbulence, measurement noise, and delays. After being synthesized off-line, the controllers allow the aircraft to follow prescribed geometrically defined paths bounded by a maximum curvature. The controllers presented within are found to be robust to the disturbances and uncertainties in the simulation environment. A robust analysis framework for mathematical validation of flight control systems is also presented. The framework is specifically developed for the complete uncertainty characterization, quantification, and analysis of small fixed-wing UAS. The analytical approach presented within is based on integral quadratic constraint (IQC) analysis methods and uses linear fractional transformations (LFTs) on uncertainties to represent system models. The IQC approach can handle a wide range of uncertainties, including static and dynamic, linear time-invariant and linear time-varying perturbations. While IQC-based uncertainty analysis has a sound theoretical foundation, it has thus far mostly been applied to academic examples, and there are major challenges when it comes to applying this approach to complex engineering systems, such as UAS. The difficulty mainly lies in appropriately characterizing and quantifying the uncertainties such that the resulting uncertain model is representative of the physical system without being overly conservative, and the associated computational problem is tractable. These challenges are addressed by applying IQC-based analysis tools to analyze the robustness of the Telemaster UAS flight control system. Specifically, uncertainties are characterized and quantified based on mathematical models and flight test data obtained in house for the Telemaster platform and custom autopilot. IQC-based analysis is performed on several time-invariant H∞ controllers along with various sets of uncertainties aimed at providing valuable information for use in controller analysis, controller synthesis, and comparison of multiple controllers. The proposed framework is also transferable to other fixed-wing UAS platforms, effectively taking IQC-based analysis beyond academic examples to practical application in UAS control design and airworthiness certification. IQC-based analysis problems are traditionally solved using convex optimization techniques, which can be slow and memory intensive for large problems. An oracle for discrete-time IQC analysis problems is presented to facilitate the use of a cutting plane algorithm in lieu of convex optimization in order to solve large uncertainty analysis problems relatively quickly, and with reasonable computational effort. The oracle is reformulated to a skew-Hamiltonian/Hamiltonian eigenvalue problem in order to improve the robustness of eigenvalue calculations by eliminating unnecessary matrix multiplications and inverses. Furthermore, fast, structure exploiting eigensolvers can be employed with the skew-Hamiltonian/Hamiltonian oracle to accurately determine critical frequencies when solving IQC problems. Applicable solution algorithms utilizing the IQC oracle are briefly presented, and an example shows that these algorithms can solve large problems significantly faster than convex optimization techniques. Finally, a large complex engineering system is analyzed using the oracle and a cutting-plane algorithm. Analysis of the same system using the same computer hardware failed when employing convex optimization techniques. / Ph. D.

<p>Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. The most PFC did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.</p>