Response-threshold methods stand out among the different developed swarm-like methodologies that address the task allocation problem, which must be faced in multi-robot systems in order to assign to each robot the best task to perform at each instant of time. In many real missions the tasks have associated deadlines. However, the literature only contains a few swarm methodologies, and thus response-threshold methods, tackling tasks with deadlines. Motivated by this fact, in this paper, we propose a new task allocation strategy inspired by response-threshold methods which deals with tasks with time deadlines, models stimuli using fuzzy sets and, in addition, in which each robot makes the decision about the best task to perform through the celebrated Bellman-Zadeh fuzzy optimization technique. An extensive number of simulations have been carried out in order to evaluate the quantitative performance of the swarm system based on the new approach. The results confirm that the proposed mathematical approach is able to model the evolution of the system when tasks with deadlines are under consideration. We have also observed competitive performance on a fleet of real robots, which corroborates the results derived from the simulations.

This paper shows that a reasonably better network bandwidth utilization in IPTV or general video delivery networks can be obtained with an automated RSVP-TE LSP reservation mechanism and by adding some video-awareness “intelligence” to certain Label Switching Routers which are part of the service provider's MPLS core network. We have implemented this intelligence on Juniper routers taking advantage of the JUNOS SDK, deployed the solution on a network test bed, and performed some measurements to validate the previous statement.

The paper proposes a method that uses topological information to guide the path search in any 2D workspace. As a first contribution, we have extended the method proposed by Jenkins to generate the topological information by taking into consideration the constraints of the workspace during the construction of the topological environment, which is then used to compute topological paths. As a second contribution, a planner based on the Rapidly-exploring Random Tree (RRT), called Homotopic RRT (HRRT) is proposed to use the topological information to guide the path search in the workspace. Finally, simulated and real results with an Autonomous Underwater Vehicle (AUV) are presented showing the feasibility of the proposal. Comparison with well-known path planning algorithms has also been included.

In recent years, anytime algorithms have shown to be a good solution for planning a path in domains with severe restrictions regarding the time for deliberation. They typically operate by quickly finding a highly suboptimal path first, and then improving it until the available time runs out. In this paper, we propose a novel anytime approach called ABUG that performs much more efficiently than the competing strategies. ABUG is based on an improved version of a member of the popular family of algorithms known as Bug. A formal analysis of the planner is provided and several relevant properties of ABUG are identified. Besides, as done in some heuristic-based anytime approaches, we define bounds on the quality/length of the paths returned by the algorithm. Finally, in order to demonstrate the computational savings associated with the proposal, a comparative study involving a set of well-known path-planning techniques is also carried out.

Reactive mobile robot navigation based on potential field methods has shown to be a good solution for dealing with both unknown and dynamic environments, where timely responses are required. Unfortunately, the complexity of the tasks which can be successfully carried out is restricted by the inherent shortcomings of the approach such as trapping situations due to local minima, difficulties passing among closely spaced obstacles, oscillations in narrow corridors, etc. The first of the aforementioned limitations was fully overcome in J. Antich and A. Ortiz (2005) by applying the two new principles of traversability and tenacity in the context of artificial potential fields. As a result, navigation was achieved in very difficult scenarios, even including maze-like environments. In this paper, continuing that previous work, it is intended to step forward by ensuring, whenever possible, that the target point is attained for any mission, setting, at the same time, an upper bound regarding the length of the resultant robot's path

Reactive mobile robot navigation based on potential field methods has shown to be a good solution for dealing with unknown and dynamic environments, where timely responses are required. Unfortunately, the complexity of the tasks which can successfully be carried out is restricted by the inherent shortcomings of the approach such as trapping situations due to local minima, difficulties passing among closely spaced obstacles, oscillations in narrow corridors, etc... This paper proposes a novel strategy which overcomes totally the first limitation and partially the others by computing an adaptive navigation function on the basis of such artificial potential fields. As a result, navigation is achieved in very difficult scenarios such as maze-like environments. A comparative study on the path length performance of our proposal with regard to other algorithms from the related literature is also presented. Both simulation and real tests are performed.

Nowadays, the surveillance and inspection of underwater installations, such as power or telecommunication cables and pipelines, are carried out by trained operators who, from the surface, control a Remotely Operated Vehicle (ROV) with cameras mounted over it. This is a tedious, time-consuming, and expensive task, prone to errors mainly because of loss of attention or fatigue of the operator and also due to the typical low quality of seabed images. In this article, a reactive control architecture for visually guiding an Autonomous Underwater Vehicle (AUV) to detect and track a cable, or pipeline, laid on the seabed is presented together with a set of experimental results. Such results were obtained by using a three-dimensional simulation environment that incorporates the hydrodynamic model of a real underwater vehicle called GARBI. Additionally, a rapidly convergent off-line tuning method based on evolutionary theory is proposed and discussed. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 477–498, 2005.