The increasing popularity of electronic vehicles has spurred a demand for EV charging infrastructure. In the United States alone, over 160,000 public and private charging ports have been installed. This has stoked fear of potential grid issues in the future. Meanwhile, companies, specifically building owners are also seeing the opportunity to leverage EV batteries as energy stores to serve as buffers against the electric grid. The main idea is to influence and control charging behavior to provide a certain level of energy resiliency and demand responsiveness to the building from grid events while ensuring that they meet the demands of EV users. However, managing and co-optimizing energy requirements of EVs and cost-saving measures of building owners is a difficult task. First, user behavior and grid uncertainty contribute greatly to the potential effectiveness of different policies. Second, different charger configurations can have drastically different effects on the cost. Therefore, we propose a complete end-to-end discrete event simulator for vehicle-to-building charging optimization. This software is aimed at building owners and EV manufacturers such as Nissan, looking to deploy their charging stations with state-of-the-art optimization algorithms. We provide a complete solution that allows the owners to train, evaluate, introduce uncertainty, and benchmark policies on their datasets. Lastly, we discuss the potential for extending our work with other vehicle-to-grid deployments.

We present a solution for intelligent planning of engine activations for series hybrid electric vehicles (HEVs), Beyond minimizing energy expenditure, other real-world objectives must be incorporated, such as minimizing the perceived engine noise and the frequency of mode transitions between activation and deactivation. We model this problem as a multiobjective stochastic shortest path (MOSSP) problem that takes a vehicle model and navigation map as input and outputs a engine activation policy. The vehicle model and navigation map are learned from GPS traces with metadata, and includes the topological road structure, traversal speeds/times, battery consumption/regeneration, and ambient noise. We analyze our results in simulation on different navigation maps generated from actual GPS traces learned from a real series HEV. Experiments in simulation demonstrate that our approach compared with the baseline system can reduce total energy expenditure (EE), namely on hills, by up to 3%; total additional noise (AN) generated by up to 15%; and total mode transition (MT) frequency by up to 12%. The approach is demonstrated on a real series hybrid vehicle, driving on real public roads.

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.

This paper presents an efficient and robust method for registration of terrain models created using stereovision on a planetary rover. Our approach projects two surface models into a virtual depth map, rendering the models, as they would be seen from a single range sensor. Correspondence is established based on which points project to the same location in the virtual range sensor. A robust norm of the deviations in observed depth is used as the objective function, and the algorithm searches for the rigid transformation, which minimizes the norm. An initial coarse search is done using rover pose information from odometry and orientation sensing. A fine search is done using Levenberg-Marquardt. Our method enables a planetary rover to keep track of designated science targets as it moves, and to hand off targets from one set of stereo cameras to another. These capabilities are essential for the rover to autonomously approach a science target and place an instrument in contact in a single command cycle.

This paper introduces the system being developed at NASA Ames Research Center, intended for the Mars '09 Smart Lander, to robustly place sensors or tools against rocks in a single communications cycle. Science targets must be assessed prior to instrument placement in order to segment them from the background and determine where, if possible, to position the instrument. An initial result of this research effort is a novel Bayesian based method for segmenting rocks from the ground using 3D data.

Automation of the search for and classification of Antarctic meteorites offers a unique case for early demonstration of robotics in a scenario analogous to geological exploratory missions to other planets and to the Earth's extremes. Moreover, the discovery of new meteorite samples is of great value because meteorites are the only significant source of extraterrestrial material available to scientists. In this paper we focus on the primary outcomes and technical lessons learned from the first field demonstration of autonomous search and in situ classification of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. Nomad's expedition proved the rudiments of science autonomy and exemplified the merits of machine learning techniques for autonomous geological classification in real-world settings. On the other hand, the expedition showcased the difficulty in executing reliable robotic deployment of science sensors and a limited performance in the speed and coverage of autonomous search.

The key to the autonomous exploration of an unknown area by a scientific robotic rover is the ability of the vehicle to autonomously recognize objects of interest and generalize about the region. This paper presents a Bayesian framework under which a mobile robot can learn how different classes of objects are distributed over a geographical region, using imperfect observations and non-random sampling. This yields dramatic improvements in classification accuracy by exploiting the interdependencies between objects in an area and allows the robot to autonomously characterize the region. This is demonstrated with data from Carnegie Mellon University's Nomad robot in Antarctica, where it traversed the ice sheet, classifying rocks in its path.

This paper describes the development and implementation of a Bayes network based system for distinguishing terrestrial rocks from meteorites from onboard the mobile robotic rover Nomad. Equipped with a color camera and spectrometer, Nomad autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine, Antarctica. This paper discusses rock classification from a robotic platform and the challenges of autonomously obtaining good sensor data in the field, while focusing on Nomad implementation.

A Bayes network based classifier for distinguishing terrestrial rocks from meteorites is implemented onboard the Nomad robot. Equipped with a camera, spectrometer and eddy current sensor, this robot searched the ice sheets of Antarctica and autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine. This paper discusses rock classification from a robotic platform, and describes the system onboard Nomad.

This paper describes the development and implementation of a combined metal and magnetic sensor suite for the detection of meteorites from a mobile robot. The relative disposition of the sensors and the overall configuration of the sensor suite with respect to the rover are such that clean measurements are achieved and interference is minimized. We validated the combined electromagnetic sensors in laboratory and field tests on a variety of stony and iron meteorites. Experiments show that both sensing modalities (eddy current and magnetic) are necessary to achieve comprehensive readings from meteorites. The sensors have detected meteorites smaller than 2 cm in size. The sensor suite is configured to allow in-situ, autonomous calibration of the sensors and to mitigate noise from the electrical components of the robot that deploys them.