With an increasing need for greater traffic safety, there is an increasing demand for means by which solutions to the traffic safety problem can be studied. The purpose of this thesis is to investigate the feasibility of using an autonomous indoor navigation system as a component in a demonstration system for studying cooperative vehicular scenarios. Our method involves developing and evaluating such a navigation system. Our navigation system uses a pre-existing localization system based on passive RFID, odometry and a particle filter. The localization system is used to estimate the robot pose, which is used to calculate a trajectory to the goal. A control system with a feedback loop is used to control the robot actuators and to drive the robot to the goal.   The results of our evaluation tests show that the system generally fulfills the performance requirements stated for the tests. There is however some uncertainty about the consistency of its performance. Results did not indicate that this was caused by the choice of localization techniques. The conclusion is that an autonomous navigation system using the aforementioned localization techniques is plausible for use in a demonstration system. However, we suggest that the system is further tested and evaluated before it is used with applications where accuracy is prioritized.

Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

In this thesis, robot navigation using the Particle Filter based FastSLAM approach for obstacle avoidance derived from a modified Velocity Potential Field method was investigated. A switching controller was developed to deal with robot’s efficient turning direction when close to obstacles. The determination of the efficient turning direction is based on the local map robot derived from its on board local sensing. The estimation of local map and robot path was implemented using the FastSLAM approach. A particle filter was utilized to obtain estimated robot path and obstacles (local map). When robot sensed only obstacles, the estimated robot positions was regarding to obstacles based the measurement of the distance between the robot and obstacles. When the robot detected the goal, estimation of robot path will switch to estimation with regard to the goal in order to obtain better estimated robot positions. Both simulation and experimental results illustrated that estimation with regard to the goal performs better than estimation regarding only to obstacles, because when robot travelled close to the goal, the residual error between estimated robot path and the ideal robot path becomes monotonously decreasing. When robot reached the goal, the estimated robot position and the ideal robot position converge. We investigated our proposed approach in two typical robot navigation scenarios. Simulations were accomplished using MATLAB, and experiments were conducted with the help of both MATLAB and LabVIEW. In simulations and experiments, the robot successfully chose efficiently turning direction to avoid obstacles and finally reached the goal.

This thesis presents the development of an instrumented compliant wrist mechanism which serves as an interface between robotic platforms and their environments in order to detect surface positions and orientations. Although inspired by similar existing devices, additional features such as noncontact distance estimations, a simplified physical structure, and wireless operation were incorporated into the design. The primary role envisioned for this mechanism was for enabling robotic manipulators to perform surface following tasks prior to contact as this was one requirement of a larger project involving inspection of surfaces. The information produced by the compliant wrist system can be used to guide robotic devices in their workspace by providing real-time proximity detection and collision detection of objects. Compliance in robotic devices has attracted the attention of many researchers due to the multitude of benefits it offers. In the scope of this work, the main advantage of compliance is that it allows rigid structures to come into contact with possibly fragile objects. Combined with instrumentation for detecting the deflections produced by this compliance, closed-loop control can be achieved, increasing the number of viable applications for an initially open-loop system. Custom fabrication of a prototype device was completed to physically test operation of the designed system. The prototype incorporates a microcontroller to govern the internal operations of the device such as sensor data collection and processing. By performing many computation tasks directly on the device, robotic controllers are able to dedicate more of their time to more important tasks such as path planning and object avoidance by using the pre-conditioned compliant device data. Extensive work has also gone into the refinement of sensor signals coming from the key infrared distance measurement sensors used in the device. A calibration procedure was developed to decrease inter-sensor variability due to the method of manufacturing of these sensors. Noise reduction in the signals is achieved via a digital filtering process. The evaluation of the performance of the device is achieved through the collection of a large amount of sensor data for use in characterisation of the sensor and overall system behavior. This comes in the form of a statistical analysis of the sensor outputs to determine signal stability and accuracy. Additionally, the operation of the device is validated by its integration onto a manipulator robot and incorporating the data generated into the robot’s control loop.

The diploma thesis is focused on the topic of global satellite navigation of mobile robots. The paper describes the principle of currently available global satellite navigation systems. The main element of the thesis is the proposal of mobile robot navigation algorithm. An integral part is also the design of a mobile robot to verify the functionality of the navigation algorithm. The robot software program is described. At the end, everything is verified by real experiments.

The master’s thesis implements localization and navigation routines for mobile robot B2 in order to operate autonomously in an environment described by a road map only. The ROS framework was used for developing new software. The research part describes possible approaches to localization problem and summarizes ROS packages with localization and navigation software. The following part includes communication with the robot’s sensor modules and data processing from LIDAR, IMU and camera. The localization package robot_localization based on Kalman filter is implemented and setting of the navigation stack navigation is proposed, aiming to robot’s autonomous outdoor navigation. Implemented functions were tested in park environment and they are evaluated in this master's thesis too.

This thesis implements solution for automatic docking for a mobile robot using visual markers. After initial survey of already implemented works, new docking solution is proposed. Feasibility of the solution is verified with tests of marker detection precision. The implementation is tested in a simulation and with a real robot. The functionality of the proposed solution is verified by long-term tests. The result of this work is robot’s ability to navigate known environment to find and dock a charging station. After charging the robot is able to safely disconnect from the station.

This master’s thesis deals with the navigation of a mobile robot that uses the ROS framework. The aim is to improve the ability of the existing B2 robot to move autonomously outdoors. The theoretical part contains a description of the navigation core, which consists of the move_base library and the packages used for planning. The practical part describe the aws of the existing solution, the design and implementation of changes and the results of subsequent testing in the urban park environment.

Diploma thesis deals with image processing for outdoor environment mobile robot. In first part, the problem is analyzed, general solution is proposed and suitable image processing methods are presented. In second part presented methods are tested and methods with best results are proposed. In third part is particular solution tested on real data.

This thesis deals with the design and implementation of autonomous robot with using of the platform ROS. Its goal is to get to know the ROS and use it to implement autonomous control of real robot Leela.