Mars Explorer Experiments: Ideas from Data Mining Point of View

Abstract

In the context of the "Mars Explorer Experiments," a set of autonomous agents (vehicles) must navigate an unknown, obstacle-filled terrain to locate and collect rock samples, with limited communication between agents and no prior detailed map of the planet. This paper explores the application of data mining techniques in the development of autonomous vehicle control architectures for Mars exploration, specifically focused on the task of collecting precious rock samples according to three types of agents (Cooperative Agents, Non-Cooperative Agents, Subsumption Architecture ) through describing the agent with the data mining point of view (problem statement in agent, solution using data mining algorithms, discussion problem and solution from data mining point of view). A significant portion of the paper discusses how data mining approaches such as clustering, reinforcement learning, anomaly detection, and pattern mining can be employed to improve agent coordination, exploration strategies, and real-time decision-making in dynamic and uncertain environments. Incorporating data mining algorithms into 'Mars exploration experiments' shows a hopeful way to boost the performance and decision-making abilities of autonomous agents on Mars. The paper shows that the data mining algorithms are not just beneficial but essential in developing intelligent, cooperative, and autonomous systems for Mars exploration vehicles.