Dviračių struktūrinio vientisumo bandymai atliekami vadovaujantis reikalavimais, aprašytais standarte EN 14764:2005:E. Bandymo metu svarsčiais apkrautas dviratis juda per kliūtis, kurios imituoja kelio nelygumus. Bandymai taip pat atliekami vietoj svarsčių naudojant užspaudimą pneumatiniais cilindrais. Šiame darbe pateikiamas bandymo sąlygas atitinkantis dviračio dinamikos rato dinamikos analitinis tyrimas, kurį taikant galima įvertinti skirtingų apkrovos būdų įtaką bandymo sąlygoms ir rezultatų tikslumui. / Main conditions of bicycle structural integrity test are presented in European standard EN 14764:2005:E. The fully-assembled bicycle is mounted on a test machine. Definite weights are applied to it. The bicycle is moved over the obstacles that simulate roughness of the way. The load to bicycle can also be produced by system of pneumatic cylinders. Analytical modeling of the dynamics of bicycle wheel rolled over the obstacle corresponded to the test condition is presented in the work. It allows analytical research of test condition with regard to different loading types and estimation of test result accuracy.

Independent mobility is critical to quality of life for people of all ages, and impaired mobility leaves one with both physical and mental disadvantages. Unfortunately, there are some individuals unable to operate an electric wheelchair due to physical, perceptual, or cognitive deficits. The prime objective of this research was to develop a prototype system which can provide mobility assistant to individuals who would otherwise find it difficult or impossible to operate a power wheelchair. To accomplish this goal, a prototype system consisting of several components including an embedded microcontroller and multiple sensors has been designed which can be added to a standard power wheelchair and make it smart. The control system algorithm designed for this prototype model is based on the fuzzy logic control theory and its main purpose is to augment the user ability to navigate the wheelchair and will provide a safe and comfortable journey to the user.

Locomotion automation is a very challenging and complex problem to solve. Besides the obvious navigation problems, there are also problems regarding the environment in which navigation has to be performed. Terrains with obstacles such as rocks, steps or high inclinations, among others, pose serious difficulties to normal wheeled vehicles. The flexibility of legged locomotion is ideal for these types of terrains but this alternate form of locomotion brings with it its own challenges to be solved, caused by the high number of degrees of freedom inherent to it. This problem is usually computationally intensive, so an alternative, using simple and hardware amenable bio-inspired systems, was studied. The goal of this thesis was to investigate if using a biologically inspired learning algorithm, integrated in a fully biologically inspired system, can improve its performance on irregular terrain by adapting its gait to deal with obstacles in its path. At first, two different versions of a learning algorithm based on unsupervised reinforcement learning were developed and evaluated. These systems worked by correlating different events and using them to adjust the behaviour of the system so that it predicts difficult situations and adapts to them beforehand. The difference between these versions was the implementation of a mechanism that allowed for some correlations to be forgotten and suppressed by stronger ones. Secondly, a depth from motion system was tested with unsatisfactory results. The source of the problems are analysed and discussed. An alternative system based on stereo vision was implemented, together with an obstacle detection system based on neuron and synaptic models. It is shown that this system is able to detect obstacles in the path of the robot. After the individual systems were completed, they were integrated together and the system performance was evaluated in a series of 3D simulations using various scenarios. These simulations allowed to conclude that both learning systems were able to adapt to simple scenarios but only the one capable of forgetting past correlations was able to adjust correctly in the more complex experiments.

The ability to navigate with limited vision is a skill that is often employed in our daily lives. Navigating without vision to a remembered target has previously been studied. However, not much is known about the attention required to perform blind navigation. We examined the effect of aging and presence of obstacles on the attentional demands of blind navigation. We evaluated reaction time, navigation errors and average walking speed in an 8 meter walking path, with or without obstacles, in the absence of vision. Results showed that older participants had increased reaction time and increased linear distance travelled as opposed to young participants, that obstacles increased reaction time and decreased average walking speed in all participants, and that emitting the reaction time stimulus early in the trial increased the linear distance travelled. Interpretation of the results suggests that aging and presence of obstacles augments the attentional demands of blind navigation.

Many potential indoor applications exist for autonomous vehicles, such as automated surveillance, inspection, and document delivery. A key requirement for autonomous operation is for the vehicles to be able to detect and map obstacles in order to avoid collisions. This work develops a comprehensive 3D scene reconstruction algorithm based on known vehicle motion and vision data that is specifically tailored to the indoor environment. Visible light cameras are one of the many sensors available for capturing information from the environment, and their key advantages over other sensors are that they are light weight, power efficient, cost effective, and provide abundant information about the scene. The emphasis on 3D indoor mapping enables the assumption that a large majority of the area to be mapped is comprised of planar surfaces such as floors, walls and ceilings, which can be exploited to simplify the complex task of dense reconstruction of the environment from monocular vision data. In this thesis, the Planar Surface Reconstruction (PSR) algorithm is presented. It extracts surface information from images and combines it with 3D point estimates in order to generate a reliable and complete environment map. It was designed to be used for single cameras with the primary assumptions that the objects in the environment are flat, static and chromatically unique. The algorithm finds and tracks Scale Invariant Feature Transform (SIFT) features from a sequence of images to calculate 3D point estimates. The individual surface information is extracted using a combination of the Kuwahara filter and mean shift segmentation, which is then coupled with the 3D point estimates to fit these surfaces in the environment map. The resultant map consists of both surfaces and points that are assumed to represent obstacles in the scene. A ground vehicle platform was developed for the real-time implementation of the algorithm and experiments were done to assess the PSR algorithm. Both clean and cluttered scenarios were used to evaluate the quality of the surfaces generated from the algorithm. The clean scenario satisfies the primary assumptions underlying the PSR algorithm, and as a result produced accurate surface details of the scene, while the cluttered scenario generated lower quality, but still promising, results. The significance behind these findings is that it is shown that incorporating object surface recognition into dense 3D reconstruction can significantly improve the overall quality of the environment map.

Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

Drones or UAVs are quickly becoming a bigger part of today's society. Delivery servicesand transportation are elds were big development is being done. For the UAVs to beable to perform its given tasks safely more and more sensors are implemented.This report covers the development and implementation of a sensor system to helpan UAV to keep a xed altitude and provide proximity measurements of the environ-ment to avoid obstacles. The system is build around the ATmega328P microprocessorand uses I2C to communicate with the sensors. Measurements are ltered and pub-lished into ROS where the autopilot can reach the measurements and make decisionsbased on the readings. Additionally, simple algorithms to avoid obstacles have beenimplemented and simulated in the simulation software Gazebo. The altitude controlsystem which has been the main focus of the project has been implemented with goodresults in both simulation and real ight tests. The system will be used in a competi-tion held in Arizona, USA where the project team together with two other project willcompete in the prestigious CPS-challenge.

This diploma work deals with what influence the colour (red, yellow, green and blue) of an obstacle has on the jumping performance of horses. Eleven horses of different ages and breeds which are used for both recreational as well as sport riding purposes participated in the trial. The success of the horses at jumping over obstacles of variours colours was evaluated in relation to other selected variables, namely gender, age, height of the jump, and the type of riding surface. The horses were most successful at jumping yellow (96.4%) and red (95.2%) obstacles, whilst being less successful over green (85.7%) and blue (83.3%) ones. A successful jump included those where the front or hind hoof touched the obstacle without actually knocking the obstacle down. The values for touching the obstacle with the front or hind hoof were approximately the same for the red (7.1%) and green (9.5%) obstacles. With the yellow obstacles, there was a higher percentage of touching with the front hoof (11.9%) than the hind hoof (4.7%). With the blue obstacles, there was also a higher percentage of touching with the front hoof (10.7%) than the hind hoof (9.5%), although the difference was not as marked as with the yellow obstacles. The influence of the other selected variables on the jumping performance of the horses over the different coloured obstacles was diverse. The values of the correlation coefficients for gender and the type of riding surface did not prove there was a statistically significant influence of the horse´s success. In contrast, statistically significant relationships were proven for the height of a jump which was green, with a correlation coefficient rxy = -0.236+ (i.e. the higher the green jump the less successful the jumps), or yellow, with a correlation coefficient rxy = -0.377+++ (i.e. the older the horse the less successful the jumps).

xiii, 122 p. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Currently the information on attention-balance control interactions following concussion is incomplete and not given particular consideration during clinical examinations of concussion. The purposes of this dissertation were to (1) test different gait paradigms for their sensitivity of identifying concussion symptoms and to (2) test how individual components of attention interact with gait performance. The long-term goal of this study is to establish more functional and succinct protocols for return-to-play decisions. Grade II (AAN guidelines) concussed individuals were recruited to participate in testing at 2, 6, 14, and 28 days post-injury. Gait and components of attention were analyzed during each session through a number of different paradigms. Control subjects were matched by stature, age, and athletic participation. The results indicate that the dynamic balance deficits following a concussion are immediately identified with an attention dividing gait task. Obstacle crossing identified more conservative adaptations 2 weeks after injury. A task combining the two did not clearly identify concussion deficits. Two components of attention showed promise as interacting with gait to cause balance deficits. The spatial orientation component showed an interaction with obstacle avoidance indicating that the same concussed individuals that had poor spatial orientation of attention also came closer to hitting the obstacle during crossings. An analysis of divided attention showed that concussed individuals performing poorly in one task also performed poorly in the other during a dual-task paradigm, but during any one particular trial there was a trade-off between task performances, which was not present in control individuals. The findings of this dissertation point to the use of a divided attention task to distinguish concussed individuals from healthy individuals immediately after a possible injurious event. How several different components of attention interact with gait performance is identified. Finally, if a concussion has occurred, an obstacle crossing task might be suitable for a long-term analysis of full recovery of balance control. Ultimately, it is my hope that the information provided here will lead to functionally relevant and clinically executable tests of concussed individuals before they are placed in harm's way due purely to an incomplete diagnosis of their injuries. / Adviser: Li-Shan Chou

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