Toolpath Smoothing using Clothoids for High Speed CNC Machines

As a result of this research, new methods for CNC toolpath smoothing were developed. Utilising these methods can increase the speed, decrease vibrations and improve the cut quality of a CNC machine. In the developed techniques, Euler spirals have been used to smooth the corners

Path Planning Based on Parametric Curves

Parametric curves are extensively used in engineering. The most commonly used parametric curves are, Bézier, B-splines, (NURBSs), and rational Bézier. Each and every one of them has special features, being the main difference between them the complexity of their mathematical definition. While Bézier curves are the simplest ones, B-splines or NURBSs are more complex. In mobile robotics, two main problems have been addressed with parametric curves. The first one is the definition of an initial trajectory for a mobile robot from a start location to a goal. The path has to be a continuous curve, smooth and easy to manipulate, and the properties of the parametric curves meet these requirements. The second one is the modification of the initial trajectory in real time attending to the dynamic properties of the environment. Parametric curves are capable of enhancing the trajectories produced by path planning algorithms adapting them to the kinematic properties of the robot. In order to avoid obstacles, the shape modification of parametric curves is required. In this chapter, an algorithm is proposed for computing an initial Bézier trajectory of a mobile robot and subsequently modifies it in real time in order to avoid obstacles in a dynamic environment

Clothoid-Based Three-Dimensional Curve for Attitude Planning

Interest in flying robots, also known as unmanned aerial vehicles (UAVs), has grown during last years in both military and civil fields [1, 2]. The same happens to autonomous underwater vehicles (AUVs) [3]. These vehicles, UAVs and AUVs, offer a wide variety of possible applications and challenges, such as control, guidance or navigation [2, 3]. In this sense, heading and attitude control in UAVs is very important [4], particularly relevant in airplanes (fixed-wing flying vehicles), because they are strongly non-linear, coupled, and tend to be underactuated systems with non-holonomic constraints. Hence, designing a good attitude controller is a difficult task [5, 6, 7, 8, 9], where stability must be taken into account by the controller [10]. Indeed, if the reference is too demanding for the controller or non-achievable because its dynamics is too fast, the vehicle might become unstable. In order to address this issue, autonomous navigation systems usually include a high-level path planner to generate smooth reference trajectories to be followed by the vehicle using a low-level controller. Usually a set of waypoints is given in GPS coordinates, normally from a map, in order to apply a smooth point-to-point control trajectory [11, 12]

Development of vision-based soft sensing techniques with training in virtual environment for autonomous vehicle control

The goal of this master thesis is to develop an original approach to lane estimation for scaled vehicles using a front-mounted camera and convolutional neural networks. The key components of this estimation process are the fact that all the training is performed in simulation using a noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an efficient and responsive way, while being very accurate. The heading error of the standard pure pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen as training path for its several advantages in the analyzed scenario. Different performance metrics are evaluated and the standard deviation of the error is found to be the more effective. An analysis on the hyperparameters (image dimension, lookahead distance, training variability, and others) is performed in order to find the best combinations and evaluate the impact of each parameter. From the results in a real world scenario a very small network and image and a very high training variability resulted as the best overall combination, with the network complexity and training variability playing a major role in the accuracy of the system. The whole process is finally tested in a real life control loop achieving very good performance, allowing for precise lane tracking using delayless local estimation.The goal of this master thesis is to develop an original approach to lane estimation for scaled vehicles using a front-mounted camera and convolutional neural networks. The key components of this estimation process are the fact that all the training is performed in simulation using a noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an efficient and responsive way, while being very accurate. The heading error of the standard pure pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen as training path for its several advantages in the analyzed scenario. Different performance metrics are evaluated and the standard deviation of the error is found to be the more effective. An analysis on the hyperparameters (image dimension, lookahead distance, training variability, and others) is performed in order to find the best combinations and evaluate the impact of each parameter. From the results in a real world scenario a very small network and image and a very high training variability resulted as the best overall combination, with the network complexity and training variability playing a major role in the accuracy of the system. The whole process is finally tested in a real life control loop achieving very good performance, allowing for precise lane tracking using delayless local estimation

Development of an Integrated Intelligent Multi -Objective Framework for UAV Trajectory Generation

This thesis explores a variety of path planning and trajectory generation schemes intended for small, fixed-wing Unmanned Aerial Vehicles. Throughout this analysis, discrete and pose-based methods are investigated. Pose-based methods are the focus of this research due to their increased flexibility and typically lower computational overhead.;Path planning in 3 dimensions is also performed. The 3D Dubins methodology presented is an extension of a previously suggested approach and addresses both the mathematical formulation of the methodology, as well as an assessment of numerical issues encountered and the solutions implemented for these.;The main contribution of this thesis is a 3-dimensional clothoid trajectory generation algorithm, which produces flyable paths of continuous curvature to ensure a more followable commanded path. This methodology is an extension of the 3D Dubins method and the 2D clothoid method, which have been implemented herein. To ensure flyability of trajectories produced by 3D pose-based trajectory generation methodologies, a set of criteria are specified to limit the possible solutions to only those flyable by the aircraft. Additionally, several assumptions are made concerning the motion of the aircraft in order to simplify the path generation problem.;The 2D and 3D clothoid and Dubins trajectory planners are demonstrated through a trajectory tracking performance comparison between first the 2D Dubins and 2D clothoid methods using a position proportional-integral-derivative controller, then the 3D Dubins and 3D clothoid methods using both a position proportional-integral-derivative controller and an outer-loop non-linear dynamic inversion controller, within the WVU UAV Simulation Environment. These comparisons are demonstrated for both nominal and off-nominal conditions, and show that for both 2D and 3D implementations, the clothoid path planners yields paths with better trajectory tracking performance as compared to the Dubins path planners.;Finally, to increase the effectiveness and autonomy of these pose-based trajectory generation methodologies, an immunity-based evolutionary optimization algorithm is developed to select a viable and locally-optimal trajectory through an environment while observing desired points of interest and minimizing threat exposure, path length, and estimated fuel consumption. The algorithm is effective for both 2D and 3D routes, as well as combinations thereof. A brief demonstration is provided for this algorithm. Due to the calculation time requirements, this algorithm is recommended for offline use

Generación de Trayectorias de Curvatura Continua para el Seguimiento de Líneas basado en Visión Artificial

Desarrollo matemático y análisis de nuevas técnicas para la generación de trayectorias de curvatura continua aplicado al problema del seguimiento de línea con curvatura y brusquedad acotadas.Girbés Juan, V. (2010). Generación de Trayectorias de Curvatura Continua para el Seguimiento de Líneas basado en Visión Artificial. http://hdl.handle.net/10251/12881Archivo delegad

Gamification for Maths and Physics in University Degrees through a Transportation Challenge

Our society is immersed in the Fourth Industrial Revolution due to the fast evolution of the new technologies that are modifying the labor market. In the near future, technologies related to Industry 4.0 will produce totally new goods and services. Therefore, the educational systems should adapt their programs to the future needs of an uncertain labor market. In particular, mathematics will play a key role in future jobs and there is a strong need to connect its teaching methodologies to the new technological scene. This work uses the STEAM approach (science, technology, engineering, arts and mathematics) along with active methodologies and educational robotics with the aim of developing a new strategy for the application of mathematics and physics in an engineering degree. In particular, a transportation challenge is posed to tackle the teaching–learning process of the Bézier curves and their applications in physics. A pilot project is developed using a LEGO EV3 robot and an active methodology, where students become the center of the learning process. The experimental results of the pilot study indicate an increase in the motivation due to the use of robots and the realistic context of the challenge

Sketch-based path design

We first present a novel approach to sketching 2D curves with minimally varying curvature as piecewise clothoids. A stable and efficient algorithm fits a sketched piecewise linear curve using a number of clothoid segments with G2 continuity based on a specified error tolerance. We then present a system for conceptually sketching 3D layouts for road and other path networks. Our system makes four key contributions. First, we generate paths with piecewise linear curvature by fitting 2D clothoid curves to strokes sketched on a terrain. Second, the height of paths above the terrain is automatically determined using a new constraint optimization formulation of the occlusion relationships between sketched strokes. Third, we present the break-out lens, a novel widget inspired by break-out views used in engineering visualization, to facilitate the in-context and interactive manipulation of paths from alternate view points. Finally, our path construction is terrain sensitive. ii Acknowledgements I would like to acknowledge the efforts of my supervisor, Karan Singh, and thank him for his guidance over the duration of the Masters program. I learned much from him a