An approach for coordinating of the cooperative mapping in a self-adaptive formation system based on a modification of the ant colony algorithm

In this work, an approach for cooperative and distributed mapping in a self-adaptive formation system based on a modified version of the ant colony optimization algorithm is proposed. The strategy is distributed, decentralized, real time and it is applied to tasks in which formation characteristic is an essential requirement. The coordination system’s design is inspired by the biological mechanisms that define a social organization in collective systems, specifically, the ant colony system. Voronoi tessalation and Delaunay triangulation techniques are used to model the formation strategy. The approach is adaptable for scenarios with suffer changes in the structure of the environment. The performance of the system is evaluated using a simulator. Simulation results show that the cooperative mapping is efficient, the trials are performed considering an indoor environment. Besides results show that the proposed formation approach is able to rearrange spatially the robots as they navigate, changing the relative robot distances according to the spatial environment restrictions.FAPESP (Grant #2010/07955-8)CNP

Object Recognition and Localization : the Role of Tactile Sensors

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This thesis presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Sequential Filter (BRICPSF) is based on an innovative combination of a sequential filter, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in simulation and using actual hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses BRICPSF for object part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments