The ability of robotic units to autonomously navigate various terrains is critical to the advancement of robotic operation in natural environments. Next generation robots will need to adapt to their environment in order to accomplish tasks that are either too hazardous, too time consuming, or physically impossible for human-beings. Such tasks may include accurate and rapid explorations of various planets or potentially dangerous areas on Earth. Furthermore, because terrain variability typically increases as the distance that a rover traverses increases, it will be beneficial for robotic units to adapt to their surroundings. As a result, this research investigates a navigation control methodology for a multi-modal locomotive robot based upon passive perception. Surface estimation for robot reconfigurability is implemented using a region growing method, which characterizes the traversability of the terrain, in conjunction with passive perception regarding motion. A mathematical approach is then implemented that inherits human psychological aspects to direct necessary navigation behavior to control robot mobility. Physical experimentations in a simulated Mars yard are presented to validate the methodology.

This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.