27,037 research outputs found
Map building, localization and exploration for multi-robot systems
The idea of having robots performing the task for which they have been designed completely autonomously and interacting with the environment has been the main objective since the beginning of mobile robotics. In order to achieve such a degree of autonomy, it is indispensable for the robot to have a map of the environment and to know its location in it, in addition to being able to solve other problems such as motion control and path planning towards its goal. During the fulfillment of certain missions without a prior knowledge of its environment, the robot must use the inaccurate information provided by its on-board sensors to build a map at the same time it is located in it, arising the problem of Simultaneous Localization and Mapping (SLAM) extensively studied in mobile robotics. In recent years, there has been a growing interest in the use of robot teams due to their multiple benefits with respect to single-robot systems such as higher robustness, accuracy, efficiency and the possibility to cooperate to perform a task or to cover larger environments in less time. Robot formations also belongs to this field of cooperative robots, where they have to maintain a predefined structure while navigating in the environment. Despite their advantages, the complexity of autonomous multi-robot systems increases with the number of robots as a consequence of the larger amount of information available that must be handled, stored and transmitted through the communications network. Therefore, the development of these systems presents new difficulties when solving the aforementioned problems which, instead of being addressed individually for each robot, must be solved cooperatively to efficiently exploit all the information collected by the team. The design of algorithms in this multi-robot context should be directed to obtain greater scalability and performance to allow their online execution. This thesis is developed in the field of multi-robot systems and proposes solutions to the navigation, localization, mapping and path planning processes which form an autonomous system. The first part of contributions presented in this thesis is developed in the context of robot formations, which require greater team cooperation and synchronization, although they can be extended to systems without this navigation constraint. We propose localization, map refinement and exploration techniques under the assumption that the formation is provided with a map of the environment, possibly partial and inaccurate, wherein it has to carry out its commanded mission. In a second part, we propose a multi-robot SLAM approach without any assumption about the prior knowledge of a map nor the relationships between robots in which we make use of state of the art methodologies to efficiently manage the resources available in the system. The performance and efficiency of the proposed robot formation and multi-robot SLAM systems have been demonstrated through their implementation and testing both in simulations and with real robots
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
This paper addresses the motion planning problem for a team of aerial agents
under high level goals. We propose a hybrid control strategy that guarantees
the accomplishment of each agent's local goal specification, which is given as
a temporal logic formula, while guaranteeing inter-agent collision avoidance.
In particular, by defining 3-D spheres that bound the agents' volume, we extend
previous work on decentralized navigation functions and propose control laws
that navigate the agents among predefined regions of interest of the workspace
while avoiding collision with each other. This allows us to abstract the motion
of the agents as finite transition systems and, by employing standard formal
verification techniques, to derive a high-level control algorithm that
satisfies the agents' specifications. Simulation and experimental results with
quadrotors verify the validity of the proposed method.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA), Singapore, 201
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
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