Interaction Templates for Multi-Robot Systems

This work describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs) which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios which previously required a task planning method. We show improved performance over a current TMP planning approach

Development of distributed control architecture for multi-robot systems

The execution of complex tasks by teams of robots has been widely investigated in the last decades, since many operations are too risky or difficult to be performed by humans or by a single robot. The complexity and variety of applications of mobile robotics make the coordination of teams a big problem, as several topologies of control systems, from simple single processes to large networks with distributed elements that are capable of switching function, may be necessary. Although simple solutions exist, more efficient approaches use distributed communication architectures and components abstraction layers. Available proposals provide many components and interfaces, complicating their understanding and operation. This paper presents a generic control architecture that provides the developer with a small amount of elements implemented safely and on high-performance libraries. The simplicity and modularity of the proposal allow implementation of features such as control of heterogeneous robots, data source and command destination transparency and platform and language independence. The ability to support with reliability, transparency and ease the development of various scenarios of autonomous mobile robotics make the proposed architecture a powerful and valuable tool in the design and operation of these systems.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Centro de Tecnologia da Informação Renato Arche

Partage d'autorité dans un essaim de drones auto-organisé

National audienceThis paper addresses the human control of a large number of unmanned air vehicles (UAVs) for the surveillance of a sensitive outdoor area. We leverage the combination of sensor network and environment marking for swarm intelligence. This grants autonomy to the UAVs system and allows the operator to focus on noteworthy tasks like counter-intrusion. This paper presents the experimental results of the SMAART project

Architecture de coopération multi-robots

Les robots sont appelés à jouer un rôle de plus en plus important dans notre société. Leur nombre toujours croissant augmentera la quantité d’interactions entre eux et ils devront arriver à travailler en collaboration. Il est donc important de développer les bases logicielles qui permettront de faciliter ces interactions. Ce mémoire présente AIDER, une Architecture pour l’Interaction Dynamique Entre Robots. Le but de cette architecture est de permettre le développement rapide de systèmes multi-robots robustes pouvant accomplir un grand éventail de tâches. L’approche proposée est un hybride entre l’approche comportementale et l’approche multicouche et elle permet une grande flexibilité dans les interactions à l’intérieur d’un groupe de robots. Pour ce faire, AIDER permet l’échange de capacités, le partage transparent de ressources entre les robots ainsi que la possibilité d’exécuter des arbres de tâches distribués dont les branches se situent sur différents robots. Ces différents mécanismes permettent aux robots d’interagir entre eux à tous les niveaux, du partage des ressources à la prise de décision. Un scénario d’exploration planétaire a été développé afin de valider l’architecture. Son implémentation a permis de confirmer qu'AIDER permet bel et bien un développement plus facile et plus rapide de tâches multi-robots, tout en ayant des performances lui permettant d’être utilisée dans des systèmes où la puissance de calcul ou les capacités de communication sont restreintes

Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots

As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm