As sensor networks gain traction and begin to scale, we will be increasingly faced with challenges associated with managing large-scale time-series data. In this paper, we present a cloud-to-edge partitioned architecture called Respawn that is capable of serving large amounts of time-series data from a continuously updating datastore with access latencies low enough to support interactive real-time visualization. Respawn targets sensing systems where resource-constrained edge node devices may only have limited or intermittent network connections linking them to a cloud-backend. The cloud-backend provides aggregate storage and transparent dispatching of data queries to edge node devices. Data is downsampled as it enters the system creating a multi-resolution representation capable of lowlatency range-base queries. Lower-resolution aggregate data is automatically migrated from edge nodes to the cloud-backend both for improved consistency and caching. In order to further mask latency from users, edge nodes automatically identify and migrate blocks of data that contain statistically interesting features. We show through simulation and micro-benchmarking that Respawn is able to run on ARM-based edge node devices connected to a cloud-backend with the ability to serve thousands of clients and terabytes of data with sub-second latencies.

Much robotics research is carried out using either PICs and processors that are a decade or more out of date The alternative is custom built electronics that is expensive and/or must be reinvented every time a new project is begun. The XBC is a new design for a robot controller merging a modern ARM processor with an FPGA that allows high performance - especially in vision processing and motor control - for a cost similar to controllers with a fraction of its capabilities. Additionally, the XBC uses a new, and still free, software development system, already in wide use. The XBC is being mass produced (at least in research hardware terms) so it is readily available and does not require computer hardware or electronics skills in order to be obtained. This paper describes the system, its capabilities and some potential applications.

Future Mars rovers, such as the planned 2009 MSL rover, require sufficient autonomy to robustly approach rock targets and place an instrument in contact with them. It took the 1997 Sojourner Mars rover between 3 and 5 communications cycles to accomplish this. This paper describes the NASA Ames approach to robustly accomplishing single cycle instrument deployment, using the K9 prototype Mars rover. An off-board 3D site model is used to select science targets for the rover. K9 navigates to targets using deduced reckoning, and autonomously assesses the target area to determine where to place an arm mounted microscopic camera. Onboard K9 is a resource cognizant conditional executive, which extends the complexity and duration of operations that a can be accomplished without intervention from mission control.

We present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms. It also provides a framework that will simplify the integration of new technologies and enable the comparison of various elements. CLARAty consists of two distinct layers: a functional layer and a decision layer. The functional layer defines the various abstractions of the system and adapts the abstract components to real or simulated devices. It provides a framework and the algorithms for low- and mid-level autonomy. The decision layer provides the system's high-level autonomy, which reasons about global resources and mission constraints. The decision layer accesses information from the functional layer at multiple levels of granularity. We also present some of the challenges in developing interoperable software for various rover platforms.