Chemical properties of lake water can provide valuable insight into its ecology. Lakes that are permanently frozen over with ice are generally inaccessible to comprehensive exploration by humans. This paper describes the integration of several novel and existing technologies into an autonomous underwater robot, ENDURANCE, that was successfully used for gathering scientific data in West Lake Bonney in Taylor Valley, Antarctica, in December 2008. This paper focuses on three novel technological and algorithmic solutions. First, a robust position estimation system that uses an acoustic beacon to complement traditional dead-reckoning is described. Second, a novel vision-based docking algorithm for locating and ascending a vertical shaft by tracking a blinking light source is presented. Third, a novel profiling system for measuring water properties while causing minimal water disturbance is described. Finally, experimental results from the scientific missions in 2008 in West Lake Bonney are presented.

Mobile robot localization, the ability of a robot to determine its global position and orientation, continues to be a major research focus in robotics. In most past cases, such localization has been studied on wheeled robots with range finding sensors such as sonar or lasers. In this paper, we consider the more challenging scenario of a legged robot localizing with a limited field-of-view camera as its primary sensory input. We begin with a baseline implementation adapted from the literature that provides a reasonable level of competence, but that exhibits some weaknesses in real-world tests. We propose a series of practical enhancements designed to improve the robot’s sensory and actuator models that enable our robots to achieve a 50% improvement in localization accuracy over the baseline implementation. We go on to demonstrate how the accuracy improvement is even more dramatic when the robot is subjected to large unmodeled movements. These enhancements are each individually straightforward, but together they provide a roadmap for avoiding potential pitfalls when implementing Monte Carlo Localization on vision-based and/or legged robots.

Reinforcement learning has been successfully applied to several subtasks in the RoboCup simulated soccer domain. Keepaway is one such task. One notable success in the keepaway domain has been the application of SMDP Sarsa(/spl lambda/) with tile-coding function approximation. However, this success was achieved with the help of some significant task simplifications, including the delivery of complete, noise-free world-state information to the agents. Here we demonstrate that this task simplification was unnecessary: the agents are able to learn even in the presence of noisy, incomplete information. We also scale up to larger problems than have been previously tried. The main contribution of this paper is a deeper understanding of the difficulties of scaling up reinforcement learning to RoboCup soccer. We address several focused questions about the previous results with detailed experiments.