Optical flow-based perception, behavior-based control and topological path planning for mobile robots using fuzzy logic concepts

Recently, mobile robots with visual perception working in dynamic environments have been extensively investigated because this method of perception offers a large amount of environmental information. Optical flow perception is an important class of visual perception because it offers powerful perception methods and it offers both egomotion and structure from motion estimation. Especially advantageous is the fact that optical flow perception does not require a priori knowledge of the working environment and can work with minimum hardware, i.e. a mono-camera as the main navigation sensor. In this thesis, a new approach of optical flow-based perception through qualitative interpretations is developed. Compared to the classical metric approaches for optical flow perception, this approach uses much simpler arithmetic and requires less computation time because of the use of qualitative optical flow interpretations. The qualitative optical flow interpretations provide mobile robots with visual perception a more detailed image of their 3D working environment, e.g. obstacle positions and indoor object types. By using fuzzy logic for the interpretations, the optical flow perception becomes simple and intelligent in a bioinspired manner and moreover gains robustness under noisy conditions in the working environment. On the other hand, this thesis develops a generic modular structure of a behavior-based control system with three clearly separate modules for perception, motion control, and path planning. These modules are connected by simple IO interfaces. The system concept is independent of the specific type of perception. The designed behaviors are functionally classified into two separated modules, concerning collision-free motion control and goal oriented path planning. The hierarchical organization of these behaviors makes the operation of the control system more efficient and enables an easy adjustment of behaviors. Some of the behaviors use fuzzy logic concepts, which result in flexible and smooth robotic motion. Furthermore a new scheme for topological path planning in combination with fuzzy-based behaviors is developed for the goal-oriented navigation of a mobile robot. This combination allows a mobile robot to perform topological path planning in a real environment without metric information regarding its global and local positions. This enables an easy adjustment of topological path planning for different sensor perceptions or landmarks by just changing the topological map data. The performance of the optical flow-based perception embedded in the behavior-based control system with the topological path planning has been successfully tested through experiments in a real environment under most realistic conditions including relevant noise effects, e.g. unfavorable lightning conditions, non-standard objects, image processing limitations, image noise, etc.Heutzutage werden mobile Roboter zunehmend mit Kameras ausgestattet, da diese eine Vielzahl von Informationen über die Umgebung bereitstellen. Die Perzeption mit Hilfe des optischen Flusses ist eine wichtige Methode der Bildverarbeitung, da sie eine leistungsfähige Umgebungserfassung und die Nachahmung biologisch-inspirierter Prozesse erlaubt. Dabei können sowohl Informationen zur Eigenbewegung als auch Daten über die Struktur der Umgebung gewonnen werden. Besonders vorteilhaft ist hierbei einerseits die Tatsache, dass keinerlei a-priori-Informationen über die Umwelt benötigt werden und anderseits die geringen Hardwareansprüche von Kamerasystemen. So kann beispielsweise eine einfache Monokamera als Hauptsensor zur Navigation für den mobilen Roboter verwendet werden. In der vorliegenden Arbeit wird ein neuer Ansatz zur optischen Fluss basierten Perzeption mittels qualitativer Interpretation entwickelt. Verglichen mit klassischen metrischen Methoden, arbeitet der vorgestellte Ansatz dabei mit einer simpleren Arithmetik und benötigt weniger Rechenzeit. Die qualitative Verarbeitung des optischen Flusses bietet dem Roboter ein detaillierteres Bild der dreidimensionalen Arbeitsumgebung. So können beispielsweise Hindernispositionen ermittelt und Objekttypen im Innenraum erfasst werden. Durch die Verwendung von Fuzzy-Logik bei der Interpretation der visuellen Information gestaltet sich die Umgebungserfassung mit Hilfe des optischen Flusses sehr einfach und erlaubt eine bioinspirierte intelligente Entscheidungsfindung, die auch robust gegenüber realen gestörten Umgebungsbedingungen ist. Weiterhin wird in der vorliegenden Arbeit eine generische modulare Struktur für eine verhaltensbasierte Steuerung mit drei klar getrennten Modulen für Perzeption, Bewegungssteuerung und Pfadplanung vorgestellt. Diese Module werden über einfache Schnittstellen miteinander verbunden. Dadurch ist das entstandene System auch auf andere Perzeptionsmethoden mobiler Roboter anwendbar. Die realisierten Verhaltensmuster werden dabei funktionsorientiert in zwei Module eingeordnet: Ein Modul sichert hierbei die kollisionsfreie Bewegungssteuerung, ein weiteres realisiert die zielorientierte Pfadplanung. Die hierarchische Organisation dieser Verhaltensmuster ermöglicht ein effizientes und einfaches Vorgehen bei der Modifikation der hinterlegten Eigenschaften. Dabei nutzen manche dieser Verhaltensmuster wiederum Konzepte der Fuzzy-Logik, um die Roboterbewegung so flexibel und leichtgängig zu realisieren, wie es bei biologischen Systemen der Fall ist. Für die zielorientierte Navigation eines mobilen Roboters wurde in einem dritten Schwerpunkt eine neue Methode für die topologische Pfadplanung in Kombination mit Fuzzy-Logik-basierten Verhalten entwickelt. Diese Kombination ermöglicht dem Roboter die topologische Pfadplanung in einer realen Umgebung ohne jegliche Verwendung von metrischen Informationen in Bezug auf seine Position und Orientierung. Dadurch kann die Pfadplanung durch einfache Modifikationen der topologischen Kartendaten für verschiedene Perzeptionssensoren oder Landmarkenrepräsentationen angepasst werden. Die Leistungsfähigkeit der Perzeption mittels des optischen Flusses innerhalb der verhaltensbasierten Steuerung zusammen mit der topologischen Pfadplanung wird anhand von Experimenten mit einem mobilen Roboter in einer realen Umgebung gezeigt. Dabei werden auch unterschiedlichste Bedingungen, wie sich ändernden Lichtverhältnissen, unbekannten Objekten, Einschränkungen bei der Bildverarbeitung sowie Bildrauschen berücksichtigt

Vehicle navigation strategy based on behavior fusion

In this paper, a new navigation strategy based on the fusion of various behaviors to enable mobile vehicles to navigate in an unknown environment is described. The aim of this research is to fuse the two independent and sometimes conflicting behaviors: obstacle avoidance and goal seeking, such that the vehicle efficiently performs obstacle avoidance while seeking it's goal. The balance between these two behaviors is achieved by combining the control actions from the goal seeker and the obstacle avoidor through evaluating the goal vector magnitude, the minimum distance detected by the ultrasonic sensors, and the distance to the obstacle in the direction of the goal vector. Furthermore, an environment evaluator is used to enhance the adaptability of the navigator by tuning the universe of discourse of the sensor space. The new navigation strategy has been verified to be efficient in an indoor virtual environment.published_or_final_versio

Behavior-based navigation of mobile robot in unknown environments using fuzzy logic and multi-objective optimization

© 2017 SERSC. This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior's objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behaviorbased architectures in term of accuracy, smoothness, traveled distance, and time response

Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot

The control problem of four-wheeled skid steering mobile robots is quite challenging mainly because the skid steering system is an underactuated system and its mathematical model is highly uncertain. Skid steering configurations employ a differential-drive technique in which the wheels rotation is limited to around one axis and the lack of a steering wheel causes the navigation to be determined by the change of speed in either side of the robot for turning. Equal speed in both sides causes a straight-line motion. However, the implementation of the dead reckoning technique on skid-steer mobile robots will limit the precision of current robot’s position because skid-steer configuration intentionally relies on wheel slippage for normal operation and this possesses some difficulties when implementing motion control using the odometric system. The thesis describes the design of a fuzzy logic controller to compensate the dead reckoning limitation and implementation on a skid-steer mobile robot. The fuzzy controller has two inputs (angle error and distance), two outputs (translational and rotational speed) and 14 rules. These inputs are computed from the dead-reckoning method that is totally reliant on the odometry readings and data are fuzzified to be the inputs of the fuzzy controller. The outputs are the analogue voltages to the left and right motors, which drive the mobile robot. For simplicity, membership functions consisting of triangular and trapezoid shapes have been adopted. The membership functions of the fuzzy sets are chosen by trial-and-error based on experimentation. The heuristic rules control the orientation of the robot according to the information about the distances from the desired positions. The crisp output values from the fuzzy logic controller are decoded and fed into a decision module where the ratios of both sides motor voltage are determined for every smooth change in speed of the motors. To facilitate the implementation of control system, real-time execution is done in an indoor environment. Data acquisition is done in a LABVIEW and a MATLAB control algorithm is called in LABVIEW. A real mobile robot, PUTRABOT2 was used to conduct the experiment. Performance evaluation is observed from the accumulated error in orientation and its trajectory obtained after mapping the information gathered from the real world via odometry sensors. Few features such as the rise time, settling time and peak time of the output responses are analyzed. Comparisons are made between fuzzy logic and PD controllers. Comparative results among these two controllers indicate the superiority of the fuzzy approach with the ability to minimize the position and orientation errors. Moreover, the trajectory accuracy is very high and more reliable in the presence of unreliable odometry readings

Target Tracking in Mobile Robot under Uncertain Environment using Fuzzy Logic Controller

This paper discusses a design of fuzzy logic algorithm in a robot.   This algorithm is useful for the robot in seeking and reaching the target.  The robot is also accomplished with an ability to avoid obstacles. Although the fuzzy rule that is embedded to the robot is very simple, it gives a good result in target seeking and obstacles avoiding task.   The originality of this research is an approach to the rules that can simplify the task by creating faster track for the robot in uncertain environment.