International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / In this paper we propose an open multi-robot architecture that dramatically reduces the time to deployment and increases the utility value to the mainstream non-technical user. We describe a multi-robot behavior-based coordination architecture and argue its suitability in the context of general-purpose robot teams operating in dynamic and unpredictable environments. We then formalize and describe a command fusion module for the coordination of high-level behaviors of the system. The command fusion module is interfaced to our middle-ware/compiler that generates behavior selection tips from a user specified abstract description of a scenario. Finally, we utilize an example search and rescue scenario to illustrate the overall process and give preliminary results of the experiments performed on actual robots.

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

The objective of the master’s thesis work is to investigate how an autonomous underwater vehicle (AUV) should act in an underwater tunnel environment. The thesis proposes sensors, control strategies, mission statement, among others, required for tunnel assignments. A behavior-based control (BBC) strategy has been developed to control the AUV. The BBC is used in the middle level of the vehicle control, i.e. the reactive control system which describes how the AUV navigates through a tunnel, while other events are considered. The control strategy has also been separated into two parts, and these are: controlling the AUV’s heading and controlling the AUV to a desired distance from the tunnel wall. To be able to evaluate the performance of the system, a graphical user interface (GUI) has been developed. The GUI enables the operator to change control settings during simulations. Two proposed control strategies are presented with simulated results. / Syftet med examensarbetet är att undersöka hur en autonom undervattensfarkost (AUV) bör agera i en undervattenstunnel miljö. Avhandlingen föreslår sensorer, reglerstrategier, uppdragsbeskrivning med mera som krävs för tunneluppdrag. En beteendebaserad (behavior-based) reglerstrategi har utvecklats för att styra AUV:n. Reglerstrategin används i mellersta nivån i farkostens reglering, det vill säga den reaktiva regleringen som beskriver hur farkosten ska styra genom en tunnel samtidigt som andra händelser beaktas. Reglerstrategin har även delats upp i två delar: reglering av AUV:ns kurs och reglering av AUV:n till ett önskat avstånd från tunnelns vägg. För att kunna verifiera funktionaliteten av systemet så har även ett grafiskt användargränssnitt utvecklats. Gränssnittet möjliggör att man kan ändra reglerparametrar under en simulering. Två föreslagna reglerstrategier presenteras med tillhörande resultat.

The interval programming model (IvP) is a mathematical programmingmodel for representing and solving multi-objective optimizationproblems. The central characteristic of the model is the use ofpiecewise linearly defined objective functions and a solution methodthat searches through the combination space of pieces rather thanthrough the actual decision space. The piecewise functions typicallyrepresent an approximation of some underlying function, but thisconcession is balanced on the positive side by relative freedom fromfunction form assumptions as well as the assurance of global optimality.In this paper the model and solution algorithms are described, and theapplicability of IvP to certain applications arediscussed.

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono-camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono-camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.

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.