A concept study of small planetary rovers : using Tensegrity Structures on Venus

Venus is among the most enigmatic and interesting places to explore in the solar system. However, the surface of Venus is a very hostile, rocky environment with extreme temperatures, pressures, and chemical corrosivity. A planetary rover to explore the surface would be scientifically valuable, but must use unconventional methods in place of traditional robotic control and mobility. This study proposes that a tensegrity structure can provide adaptivity and control in place of a traditional mechanism and electronic controls for mobility on the surface of Venus and in other extreme environments. Tensegrity structures are light and compliant, being constructed from simple repeating rigid and flexible members and stabilized only by tension, drawing inspiration from biology and geometry, and are suitable for folding, deployment, and adaptability to terrain. They can also utilize properties of smart materials and geometry to achieve prescribed movements. Based on the needs of scientific exploration, a simple tensegrity rover can provide mobility and robustness to terrain and environmental conditions, and can be powered by environmental sources such as wind. A wide variety of tensegrity structures are possible, and some initial concepts suitable for volatile and complex environments are proposed here

Proof of concept study for small planetary rovers using tensegrity structure on Venus

Venus is a planet that we would very much like to further explore and understand. However, the surface of Venus is very hostile environment with a surface temperature averaging 452 degrees centigrade at an atmospheric pressure of 93 bar, with the atmosphere composed mainly of carbon dioxide with clouds of sulfuric acid. Missions to the surface of Venus have succeeded, but mission lifetime is extremely short, on the order of minutes to hours, until materials melt and electronic components fail due to the extreme conditions. A small rover that would be capable of exploring the surface would be scientifically valuable, but requires unconventional thinking to be able to survive the environment. We present a study of means by which to design a small planetary rover that uses a bio-inspired flexible tensegrity structure and operates mechanically to utilize the conditions of the Venusian surface in its operation. Tensegrity robots make use of geometrically-inspired and bio-inspired structures that allow multiple degree of freedom movement. These structures are sparse, composed of linked tension and compression members in a similar manner to a truss, and are very lightweight compared to traditional mobile robots and retainin the ability to flexibly adapt to terrain and fold for transport. The use of a tensegrity structure also has benefits in terms of simplicity of structure, in the sense that structural elements are simple and repeatable, held in place only by the tension of materials. This allows tensegrity rovers to utilize smart materials and structural properties in operation, without the use of complex joints and bearings to allow full-body movements. The use of flexible structures allows the rover to transform and re-configure itself to a limited extent in the field in response to environmental factors. In this paper, a concept and feasibility study of a small tensegrity rover that utilizes mechanical operation will be examined for Venus exploration, with consideration of previous missions and their lessons. The use of the rover for scientific exploration will also be justified. The results of this study will also benefit terrestrial applications that require robotic operation in the presence of extremes of temperature and pressure. The work will be used for the development of space technology for very harsh and hot planetary exploration

Planetary micro-rover operations on Mars using a Bayesian framework for inference and control

With the recent progress toward the application of commercially-available hardware to small-scale space missions, it is now becoming feasible for groups of small, efficient robots based on low-power embedded hardware to perform simple tasks on other planets in the place of large-scale, heavy and expensive robots. In this paper, we describe design and programming of the Beaver micro-rover developed for Northern Light, a Canadian initiative to send a small lander and rover to Mars to study the Martian surface and subsurface. For a small, hardware-limited rover to handle an uncertain and mostly unknown environment without constant management by human operators, we use a Bayesian network of discrete random variables as an abstraction of expert knowledge about the rover and its environment, and inference operations for control. A framework for efficient construction and inference into a Bayesian network using only the C language and fixed-point mathematics on embedded hardware has been developed for the Beaver to make intelligent decisions with minimal sensor data. We study the performance of the Beaver as it probabilistically maps a simple outdoor environment with sensor models that include uncertainty. Results indicate that the Beaver and other small and simple robotic platforms can make use of a Bayesian network to make intelligent decisions in uncertain planetary environments

Design Issues for Hexapod Walking Robots

Hexapod walking robots have attracted considerable attention for several decades. Many studies have been carried out in research centers, universities and industries. However, only in the recent past have efficient walking machines been conceived, designed and built with performances that can be suitable for practical applications. This paper gives an overview of the state of the art on hexapod walking robots by referring both to the early design solutions and the most recent achievements. Careful attention is given to the main design issues and constraints that influence the technical feasibility and operation performance. A design procedure is outlined in order to systematically design a hexapod walking robot. In particular, the proposed design procedure takes into account the main features, such as mechanical structure and leg configuration, actuating and driving systems, payload, motion conditions, and walking gait. A case study is described in order to show the effectiveness and feasibility of the proposed design procedure

Fine Grained Robotics

Fine grained robotics is the idea of solving problems utilizing multitudes of very simple machines in place of one large complex entity. Organized in the proper way, simple machines and simple behaviors can lead to emergent solutions. Just as ants and termites perform useful work and build communal structures, gnat robots can solve problems in new ways. This notion of collective intelligence, married with technologies for mass-producing small robots very cheaply will blaze new avenues in all aspects of everyday life. Building gnat robots involves not only inventing the components from which to put together systems but also developing the technologies to produce the components. This paper analyzes prototype microrobotic systems, specifically calculating torque and power requirements for three locomotion alternatives (flying, walking and swimming) for small robots. With target specifications for motors for these systems, we then review technology options and bottlenecks and sort through the tree of possibilities to pick and appropriate path along which we plan to proceed.MIT Artificial Intelligence Laborator

Building Brains for Bodies

We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We will build an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to "think'' by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation

FATMAS: a methodology to design fault-tolerant multi-agent systems

Un système multi-agent (SMA) est un système dans lequel plusieurs agents opèrent et interagissent. Chaque agent a la responsabilité d’exécuter des tâches. Cependant, chaque agent, pour diverses raisons, peut rencontrer des problèmes pendant l’exécution de ses tâches ; ce qui peut induire un disfonctionnement du SMA. Cependant, le SMA doit être en mesure de détecter les sources de problèms (d’erreurs) afin de les contrôler et ainsi continuer son exécution correctement. Un tel SMA est appelé un SMA tolérant aux fautes. Il existe deux types de sources d’erreurs pour un agent : les erreurs causées par son environnment et les erreurs dûes à sa programmation. Dans la littérature, il existe plusieurs techniques qui traitent des erreurs de programmation au niveau des agents. Cependant, ces techniques ne traitent pas des erreurs causées par l’environnement de l’agent. Tout d’abord, nous distinguons entre l’environnment d’un agent et l’environnement du SMA. L’environnement d’un agent représente toutes les composantes matérielles ou logicielles que l’agent ne peut contrôler mais avec lesquelles il interagit. Cependant, l’environnment du SMA représente toutes les composantes que le système ne contrôle pas mais avec lesquelles il interagit. Ainsi, le SMA peut contrôler certaines des composantes avec lesquelles un agent interagit. Ainsi, une composante peut appartenir à l’environnement d’un agent et ne pas appartenir à l’environnement du système. Dans ce travail, nous présentons une méthodologie de conception de SMA tolérants aux fautes, nommée FATMAS, qui permet au concepteur du SMA de détecter et de corriger, si possible, les erreurs causées par les environnements des agents. Cette méthodologie permettra ainsi de délimiter la frontière du SMA de son environnement avec lequel il interagit. La frontière du SMA est déterminée par les différentes composantes (matérielles ou logicielles) que le système contrôle. Ainsi, le SMA, à l’intérieur de sa frontière, peut corriger les erreurs provenant de ses composantes. Cependant, le SMA n’a aucun contrôle sur toutes les composantes opérant dans son environnement. La méthodologie, que nous proposons, doit couvrir les trois premières phases d’un développement logiciel qui sont l’analyse, la conception et l’implémentation tout en intégrant, dans son processus de développement, une technique permettant au concepteur du système de délimiter la frontière du SMA et ainsi détecter les sources d’erreurs et les contrôler afin que le système multi-agent soit tolérant aux fautes (SMATF). Cependant, les méthodologies de conception de SMA, référencées dans la littérature, n’intègrent pas une telle technique. FATMAS offre au concepteur du SMATF quatre modèles pour décrire et développer le SMA ainsi qu’une technique de réorganisation du système qui lui permet de détecter et de contrôler ses sources d’erreurs, et ainsi définir la frontière du SMA. Chaque modèle est associé à un micro processus qui guide le concepteur lors du développement du modèle. FATMAS offre aussi un macro-processus, qui définit le cycle de développement de la méthodologie. FATMAS se base sur un développement itératif pour identifier et déterminer les tâches à ajouter au système afin de contrôler des sources d’erreurs. À chaque itération, le concepteur évalue, selon une fonction de coût/bénéfice s’il est opportun d’ajouter de nouvelles tâches de contrôle au système. Le premier modèle est le modèle de tâches-environnement. Il est développé lors de la phase d’analyse. Il identifie les différentes tâches que les agents doivent exécuter, leurs préconditions et leurs ressources. Ce modèle permet d’identifier différentes sources de problèmes qui peuvent causer un disfonctionnement du système. Le deuxième modèle est le modèle d’agents. Il est développé lors de la phase de conception. Il décrit les agents, leurs relations, et spécifie pour chaque agent les ressources auxquelles il a le droit d’accéder. Chaque agent exécutera un ensemble de tâches identifiées dans le modèle de tâches-environnement. Le troisième modèle est le modèle d’interaction d’agents. Il est développé lors de la phase de conception. Il décrit les échanges de messages entre les agents. Le quatrième modèle est le modèle d’implémentation. Il est développé lors de la phase d’implémentation. Il décrit l’infrastructure matérielle sur laquelle le SMA va opérer ainsi que l’environnement de développement du SMA. La méthodologie inclut aussi une technique de réorganisation. Cette technique permet de délimiter la frontière du SMA et contrôler, si possible, ses sources d’erreurs. Cette technique doit intégrer trois techniques nécessaires à la conception d’un système tolérant aux fautes : une technique de prévention d’erreurs, une technique de recouvrement d’erreurs, et une technique de tolérance aux fautes. La technique de prévention d’erreurs permet de délimiter la frontière du SMA. La technique de recouvrement d’erreurs permet de proposer une architecture du SMA pour détecter les erreurs. La technique de tolérance aux fautes permet de définir une procédure de réplication d’agents et de tâches dans le SMA pour que le SMA soit tolérant aux fautes. Cette dernière technique, à l’inverse des techniques de tolérance aux fautes existantes, réplique les tâches et les agents et non seulement les agents. Elle permet ainsi de réduire la complexité du système en diminuant le nombre d’agents à répliquer. Résumé iv De même, un agent peut ne pas être en erreur mais la composante matérielle sur laquelle il est exécuté peut ne plus être fonctionnelle. Ce qui constitue une source d’erreurs pour le SMA. Il faudrait alors que le SMA continue à s’exécuter correctement malgrè le disfonctionnement d’une composante. FATMAS fournit alors un support au concepteur du système pour tenir compte de ce type d’erreurs soit en contrôlant les composantes matérielles, soit en proposant une distribution possible des agents sur les composantes matérielles disponibles pour que le disfonctionnement d’une composante matérielle n’affecte pas le fonctionnement du SMA. FATMAS permet d’identifier des sources d’erreurs lors de la phase de conception du système. Cependant, elle ne traite pas des sources d’erreurs de programmation. Ainsi, la technique de réorganization proposée dans ce travail sera validée par rapport aux sources d’erreurs identifiées lors de la phase de conception et provenant de la frontière du SMA. Nous démontrerons formellement que, si une erreur provient d’une composante que le SMA contrôle, le SMA devrait être opérationnel. Cependant, FATMAS ne certifie pas que le futur système sera toujours opérationnel car elle ne traîte pas des erreurs de programmation ou des erreurs causées par son environnement.A multi-agent system (MAS) consists of several agents interacting together. In a MAS, each agent performs several tasks. However, each agent is prone to individual failures so that it can no longer perform its tasks. This can lead the MAS to a failure. Ideally, the MAS should be able to identify the possible sources of failures and try to overcome them in order to continue operating correctly ; we say that it should be fault-tolerant. There are two kinds of sources of failures to an agent : errors originating from the environment with which the agents interacts, and programming exceptions. There are several works on fault-tolerant systems which deals with programming exceptions. However, these techniques does not allow the MAS to identify errors originating from an agent’s environment. In this thesis, we propose a design methodology, called FATMAS, which allows a MAS designer to identify errors originating from agents’ environments. Doing so, the designer can determine the sources of failures it could be able to control and those it could not. Hence, it can determine the errors it can prevent and those it cannot. Consequently, this allows the designer to determine the system’s boundary from its environment. The system boundary is the area within which the decision-taking process of the MAS has power to make things happen, or prevent them from happening.We distinguish between the system’s environment and an agent’s environment. An agent’s environment is characterized by the components (hardware or software) that the agent does not control. However, the system may control some of the agent’s environment components. Consequently, some of the agent’s environment components may not be a part of the system’s environment. The development of a fault-tolerant MAS (FTMAS) requires the use of a methodology to design FTMAS and of a reorganization technique that will allow the MAS designer to identify and control, if possible, different sources of system failure. However, current MAS design methodologies do not integrate such a technique. FATMAS provides four models used to design and implement the target system and a reorganization technique to assist the designer in identifying and controlling different sources of system’s failures. FATMAS also provides a macro process which covers the entire life cycle of the system development as well as several micro processes that guide the designer when developing each model. The macro-process is based on an iterative approach based on a cost/benefit evaluation to help the designer to decide whether to go from one iteration to another. The methodology has three phases : analysis, design, and implementation. The analysis phase develops the task-environment model. This model identifies the different tasks the agents will perform, their resources, and their preconditions. It identifies several possible sources of system failures. The design phase develops the agent model and the agent interaction model. The agent model describes the agents and their resources. Each agent performs several tasks identified in the task-environment model. The agent interaction model describes the messages exchange between agents. The implementation phase develops the implementation model, and allows an automatic code generation of Java agents. The implementation model describes the infrastructure upon which the MAS will operate and the development environment to be used when developing the MAS. The reorganization technique includes three techniques required to design a fault-tolerant system : a fault-prevention technique, a fault-recovery technique, and a fault-tolerance technique. The fault-prevention technique assists the designer in delimiting the system’s boundary. The fault-recovery technique proposes a MAS architecture allowing it to detect failures. The fault-tolerance technique is based on agent and task redundancy. Contrary to existing fault-tolerance techniques, this technique replicates tasks and agents and not only agents. Thus, it minimizes the system complexity by minimizing the number of agents operating in the system. Furthermore, FATMAS helps the designer to deal with possible physical component failures, on which the MAS will operate. It proposes a way to either control these components or to distribute the agents on these components in such a way that if a component is in failure, then the MAS could continue operating properly. The FATMAS methodology presented in this dissertation assists a designer, in its development process, to build fault-tolerant systems. It has the following main contributions : 1. it allows to identify different sources of system failure ; 2. it proposes to introduce new tasks in a MAS to control the identified sources of failures ; 3. it proposes a mechanism which automatically determines which tasks (agents) should be replicated and in which other agents ; 4. it reduces the system complexity by minimizing the replication of agents ; Abstract vii 5. it proposes a MAS reorganization technique which is embedded within the designed MAS and assists the designer to determine the system’s boundary. It proposes a MAS architecture to detect and recover from failures originating from the system boundary. Moreover, it proposes a way to distribute agents on the physical components so that the MAS could continue operating properly in case of a component failure. This could make the MAS more robust to fault prone environments. FATMAS alows to determine different sources of failures of a MAS. The MAS controls the sources of failures situated in its boundary. It does not control the sources of failures situated in its environments. Consequently, the reorganization technique proposed in this dissertation will be proven valid only in the case where the sources of failures are controlled by the MAS. However, it cannot be proven that the future system is fault-tolerant since faults originating from the environment or from coding are not dealt with

Petit robot marcheur : plateforme tout-terrain (PROMPT)

L'exploration planétaire utilise des robots à roues depuis plusieurs années maintenant. Cependant, même si elles ont du succès, ces missions spatiales sont limitées à des terrains relativement plats et sédimentaires. Les zones explorées sont très intéressantes, mais elles le sont moins au point de vue de la recherche de traces potentielles de vie lorsqu'elles sont comparées à certains endroits plus risqués. Il est entendu que les agences spatiales ne peuvent prendre le risque d'envoyer des véhicules au coût important dans ces zones à la géologie peu commune. C'est pourquoi l'exploration spatiale évoluera peut-être vers l'utilisation de petits robots à faible coût assez agiles pour explorer ces terrains riches en informations géologiques. La recherche de vie sur Mars pour expliquer notre existence fait rêver. Les contraintes de cette mission sont cependant nombreuses puisque la planète n'est pas très hospitalière. Après avoir examiné plusieurs options, le Laboratoire de robotique de l'Université Laval a développé un petit robot marcheur inspiré des insectes. Ce mémoire décrit les étapes de design que la plateforme tout-terrain a franchies, soient l'analyse, la conception, la fabrication et l'expérimentation. Le système a traversé ce processus rigoureux avec succès, mais a toujours besoin d'améliorations. C'est là tout l'intérêt du projet, envoyer un robot parfaitement fonctionnel et autonome sur Mars pour parcourir aisément cette planète qui a tant de secrets à découvrir