Unmanned Underwater Vehicles (UUVs) are commonly used in Oceanography due to their relative low cost and wide range of capabilities. Gliders are a type of UUV particularly suitable for long-range missions because of their large autonomy. They change their buoyancy to dive and climb describing a vertical saw tooth pattern, which produces an effective but low horizontal speed. Consequently, gliders are strongly sensitive to ocean currents, so they might have to adapt the heading to the current field. In this article we outline a novel path planning algorithm for gliders using ocean currents. It bases on the A* family of algorithms and incorporates a probabilistic framework. Our approach intends to alleviate some of the drawbacks that A* has with the problem at hand. Instead of discretizing the search space, a set of bearing angles is sampled at each surfacing point and the glider trajectory is integrated. We propose an Adaptive Bearing Sampling (ABS) procedure which reduces the computational time with low impact on the results, as shown by the tests run with ocean currents of a Regional Ocean Model.

Underwater gliders constitute a technology in active development, which has proven very promising in Ocean Research because of its relative low cost and long mission range. Due to their low surge speed, however, gliders are strongly affected by ocean currents, making path planning a crucial tool for this type of vehicles. In this work, we present a novel path planning algorithm for gliders based on iterative optimization that shows promising results in realistic simulations. This method reflects accurately the vehicle operation pattern and exhibits a better performance when compared with alternative approaches that are compared in this paper.

The development and maintenance of software for robotic systems is a hard task due to the complexity inherent in these systems. Besides, the resulting applications have to deal with limited resources and variable execution conditions that must be considered in order to keep an acceptable system performance. To address both problems we have integrated a set of dynamic adaptation policies inside CoolBOT, a component oriented framework for programming robotic systems. CoolBOT contributes to reduce the programming effort, promoting robustness and code reuse, while the adaptation scheme provides a dynamic modulation of system performance to meet available computational resources at runtime. In this paper we also present two demonstrators that outline the benefits of using the proposed approach in the development of real robotic applications.

Developing software for controlling robotic systems is costly due to the complexity inherent in these systems. There is a need for tools that permit a reduction in the programming efforts, aiming at the generation of modular and robust applications, and promoting software reuse. The techniques which are of common use today in other areas are not adequate to deal with the complexity associated with these systems. In this work we present CoolBOT, a component oriented framework for programming robotic systems, based on the Port Automata model that fosters controllability and observability of software components. A simple demonstrator outlines the benefits of using the proposed approach in the development of a robotic application.