Visando contribuir com o estado-da-arte de sistemas bioinspirados em formigas na robóotica, neste trabalho é abordado o problema do controle de um grupo de robôs para a solução coletiva das tarefas de exploração do ambiente e localização de objetos. Para isso, são utilizados algoritmos inspirados em colônias de formigas. O objetivo deste trabalho, portanto, é o desenvolvimento de um sistema de controle de navegação baseado em colônia de formigas para um time de robôs, de maneira que os robôs resolvam esses problemas utilizando estratégias de controle individuais e simples. Esse sistema tem como base a utilização de marcadores ou feromônios artificiais, que podem ser depositados pelos robôs para marcar determinadas posiçôes do ambiente / Aiming to advance the state-of-the-art of ant bioinspired systems in robotic applications, in this work we study the problem of controling a group of robots for solving colective tasks on environment exploration and object localization. To this end, we used algorithms inspired in ant colonies. Therefore, the objective of this work is to develop a navigation control system based on ant colony can solve the problems using simple control strategies. This system uses marks or artificial pheromones that can be released by the robots to mark specific positions in the environment

Visando contribuir com o estado-da-arte de sistemas bioinspirados em formigas na robóotica, neste trabalho é abordado o problema do controle de um grupo de robôs para a solução coletiva das tarefas de exploração do ambiente e localização de objetos. Para isso, são utilizados algoritmos inspirados em colônias de formigas. O objetivo deste trabalho, portanto, é o desenvolvimento de um sistema de controle de navegação baseado em colônia de formigas para um time de robôs, de maneira que os robôs resolvam esses problemas utilizando estratégias de controle individuais e simples. Esse sistema tem como base a utilização de marcadores ou feromônios artificiais, que podem ser depositados pelos robôs para marcar determinadas posiçôes do ambiente / Aiming to advance the state-of-the-art of ant bioinspired systems in robotic applications, in this work we study the problem of controling a group of robots for solving colective tasks on environment exploration and object localization. To this end, we used algorithms inspired in ant colonies. Therefore, the objective of this work is to develop a navigation control system based on ant colony can solve the problems using simple control strategies. This system uses marks or artificial pheromones that can be released by the robots to mark specific positions in the environment

The objective of this research is to develop a framework for multi-robot coordination and control with emphasis on human-swarm and inter-agent interactions. We focus on two problems: in the first we address how to enable a single human operator to externally influence large teams of robots. By directly imposing density functions on the environment, the user is able to abstract away the size of the swarm and manipulate it as a whole, e.g., to achieve specified geometric configurations, or to maneuver it around. In order to pursue this approach, contributions are made to the problem of coverage of time-varying density functions. In the second problem, we address the characterization of inter-agent interactions and enforcement of desired interaction patterns in a provably safe (i.e., collision free) manner, e.g., for achieving rich motion patterns in a shared space, or for mixing of sensor information. We use elements of the braid group, which allows us to symbolically characterize classes of interaction patterns. We further construct a new specification language that allows us to provide rich, temporally-layered specifications to the multi-robot mixing framework, and present algorithms that significantly reduce the search space of specification-satisfying symbols with exactness guarantees. We also synthesize provably safe controllers that generate and track trajectories to satisfy these symbolic inputs. These controllers allow us to find bounds on the amount of safe interactions that can be achieved in a given bounded domain.

Even with the advance of autonomous robotics and automation, many automated tasks still require human intervention or guidance to mediate uncertainties in the environment or to execute the complexities of a task that autonomous robots are not yet equipped to handle. As such, robot controllers are needed that utilize the strengths of both autonomous agents, adept at handling lower level control tasks, and humans, superior at handling higher-level cognitive tasks. To address this need, we develop a control theoretic framework that seeks to incorporate user commands such that user intention is preserved while an automated task is carried out by the controller. This is a novel approach in that system theoretic tools allow for analytic guarantees of feasibility and convergence to goal states which naturally lead to varying levels of autonomy. We develop a model predictive controller that takes human input, infers human intent, then applies a control that minimizes deviations from the intended human control while ensuring that the lower-level automated task is being completed. This control framework is then evaluated in a human operator study involving a shared control task with human guidance of a mobile robot for navigation. These theoretical and experimental results lay the foundation for applying this control method for human-robot cooperative control to actual human-robot tasks. Specifically, the control is applied to a Urban Search and Rescue robot task where the shared control of a quadruped rescue robot is needed to ensure static stability during human-guided leg placements in uneven terrain. This control framework is also extended to a multiple user and multiple agent system where the human operators control multiple agents such that the agents maintain a formation while allowing the human operators to manipulate the shape of the formation. User studies are also conducted to evaluate the control in multiple operator scenarios.