Learning from Sensors and Past Experience in an Autonomous Oceanographic Probe
Publication Type:
Conference PaperSource:
Twenty-Second Conference on Innovative Applications of Artificial Intelligence Conference (IAAI-10), Association for the Advancement of Artificial Intelligence (AAAI), p.1859-1864 (2010)Keywords:
PSO; Particle Swarm Optimization; CBRAbstract:
The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine re- gions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phyto- plankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the di- mensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case- Based Reasoning. We report results of experiments performed with simulated environments.