Publication type: Conferences

Evolutionary Tuning of the Control Architecture of an Underwater Cable Tracker

A Goal-directed Reactive Obstacle Avoidance Strategy with Global Proofs

Building a Qualitative Local Occupancy Grid in a new Vision-based Reactive Navigation Strategy for Mobile Robots

Bug-based T2: A New Globally Convergent Approach to Reactive Navigation

Estimation of Scene Lighting Parameters and Camera Dark Current

Using Particle Filters for Autonomous Underwater Cable Tracking

A Bayesian Approach for Tracking Undersea Narrow Telecommunication Cables

Auction Like Task Allocation and Motion Coordination Strategies for Multi-Robot Transport Tasks

In this paper we present a task allocation method based on auction mechanisms that allows to find how many robots are needed to execute a task. This number is unknown and depends on several factors. There are also different types of tasks that must be executed using different skills of the robots. It is very difficult to find a correct allocation under this conditions and at present it is an open problem. We also propose two motion coordination methods to reduce the interference effect between robots. To test our system a modification of the well know foraging task has been used. This task introduces special characteristics, not directly studied in previous work, that our method try to solve.

A Multi-Robot Task Allocation Method To Regulate Working Groups Sizes

Learning by Example: Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking