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.

No abstract

Satellite-based instruments are now routinely used to map the surface of the globe or monitor weather conditions. However, these orbital measurements of ground-based quantities are heavily influenced by external factors, such as air moisture content or surface emissivity. Detailed atmospheric models are created to compensate for these factors, but the satellite system must still be tested over a wide variety of surface conditions to validate the instrumentation and correction model. Validation and correction are particularly important for arctic environments, as the unique surface properties of packed snow and ice are poorly modeled by any other terrain type. Currently, this process is human intensive, requiring the coordinated collection of surface measurements over a number of years. A decentralized, autonomous sensor network is proposed which allows the collection of ground-based environmental measurements at a location and resolution that is optimal for the specific on-orbit sensor under investigation. A prototype sensor network has been constructed and fielded on a glacier in Alaska, illustrating the ability of such systems to properly collect and log sensor measurements, even in harsh arctic environments.

The capability to monitor natural phenomena using mobile sensing is a benefit to the Earth science community given the potentially large impact that we, as humans, can have on naturally occurring processes. Observable phenomena that fall into this category of interest range from static to dynamic in both time and space (i.e. temperature, humidity, and elevation). Such phenomena can be readily monitored using networks of mobile sensor nodes that are tasked to regions of interest by scientists. In our work, we hone in on a very specific domain, elevation changes in glacial surfaces, to demonstrate a concept applicable to any spatially distributed phenomena. Our work leverages the sensing of a vision-based SLAM odometry system and the design of robotic surveying navigation rules to reconstruct scientific areas of interest, with the goal of monitoring elevation changes in glacial regions. We validate the output from our methodology and provide results that show the reconstructed terrain error complies with acceptable mapping standards found in the scientific community.

In this paper, we present a hierarchical methodology that learns new walking gaits autonomously while operating in an uncharted environment, such as on the Mars planetary surface or in the remote Antarctica environment. The focus is to maintain persistent forward locomotion along the body axis, while navigating in natural terrain environments. The hierarchical strategy consists of a finite state machine that models the state of leg orientations coupled with a modified evolutionary algorithm to learn necessary leg movement sequences. Locomotion behavior is assessed by monitoring the robot's progress toward a specified goal location. Details of the methodology are discussed, and experimental results with a sixlegged robot are presented.