Anne Wright

Dynamic Object Capture Using Fast Vision Tracking

This article discusses the use of fast (60 frames per second) object tracking using the COGNACHROME VISION SYSTEM, produced by Newton Research Labs. The authors embedded the vision system in a small robot base to tie for first place in the Clean Up the Tennis Court event at the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. Of particular interest is that the authors entry was the only robot capable of using a gripper to capture and pick up the motorized, randomly moving squiggle ball. Other examples of robotic systems using fast vision tracking are also presented, such as a robot arm capable of catching thrown objects and the soccer-playing robot team that won the 1996 Micro Robot World Cup Soccer Tournament in Taejon, Korea.

The XBC: a modern low-cost mobile robot controller

Much robotics research is carried out using either PICs and processors that are a decade or more out of date The alternative is custom built electronics that is expensive and/or must be reinvented every time a new project is begun.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.

Two dogs, new tricks: A two-rover mission simulation using K9 and FIDO at Black Rock Summit, Nevada

[1]xa0An experiment illustrating two rovers cooperatively exploring a field site was performed at Black Rock Summit, Nevada, in May 2000. The rovers FIDO and K9 are mechanically identical prototype planetary rovers designed at the Jet Propulsion Laboratory. FIDO carried high-resolution false-color infrared and low-resolution monochrome stereo cameras and an infrared point spectrometer on a mast-mounted pointable platform, a manipulator arm equipped with a color microscopic imager, and a coring drill for sample collection. K9 carried on a mast-mounted pointable platform high-resolution color and low-resolution monochrome stereo cameras, and a Laser Induced Breakdown Spectrometer for standoff elemental analysis. A team located at Jet Propulsion Laboratory commanded the two rovers for 3 days. K9 obtained stereo images of targets, and three-dimensional models were constructed to determine the best locations for FIDO to obtain core samples. A drilling target was selected 1.5 m from the starting position of FIDO. Six command cycles and 2 m of traversing were required for FIDO to reach, drill into, and place an instrument on the target. K9 required 11 command cycles to traverse 60 m and obtain full-coverage stereo images of two rock targets along its route. Virtual reality-based visualization software called Viz provided situational awareness of the environment for both rovers. Commands to K9 were planned using Viz, resulting in improved rover performance. The results show that two rovers can be used synergistically to achieve science goals, but further testing is needed to completely explore the value of two-rover missions.

Developing an autonomy infusion infrastructure for robotic exploration

In this paper, we present an overview of the CLARAty (coupled layer architecture for robotic autonomy) architecture and describe the growth of capabilities and algorithms now available within this framework. We discuss the challenges of developing a software system with remote institutions and the lessons learned in our experience developing CLARAty with ARC, JPL, and Carnegie Mellon University. We describes the rover testbeds, in particular the K9 rover, and the integration and demonstration of new technologies enabling robust execution, single communication cycle instrument placement, fault diagnosis, and autonomous science.

Instrument deployment for Mars Rovers

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.

The importance of fast vision in winning the First Micro-Robot World Cup Soccer Tournament

Abstract This paper describes the Newton Labs entry in the 1996 MIROSOT Micro-Robot World Cup Soccer Tournament (http://www.mirosot.org), which won first place. Control of the three robot players is centralized; this requires relatively high bandwidth connections to the robots, as well as fast spatial information at the central controller. Spatial sensing is accomplished by use of the Cognachrome Vision Tracking System, which is capable of tracking the positions and orientations of several objects at the full NTSC frame rate of 60 Hz.

Respawn: A Distributed Multi-resolution Time-Series Datastore

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.

Self-Tracking: Reflections from the BodyTrack Project

Based on the author’s experiences the practice of self-tracking can empower individuals to explore and address issues in their lives. This work is inspired by examples of people who have reclaimed their wellness through an iterative process of noticing patterns of ups and downs, trying out new ideas and strategies, and observing the results. In some cases, individuals have realized that certain foods, environmental exposures, or practices have unexpected effects for them, and that adopting custom strategies can greatly improve quality of life, overcoming chronic problems. Importantly, adopting the role of investigator of their own situation appears to be transformative: people who embarked on this path changed their relationship to their health situation even before making discoveries that helped lead to symptom improvement. The author co-founded the BodyTrack project in 2010 with the goal of empowering a broader set of people to embrace this investigator role in their own lives and better address their health and wellness concerns, particularly those with complex environmental or behavioral components. The core of the BodyTrack system is an open source web service called Fluxtream (https://fluxtream.org) that allows users to aggregate, visualize, and reflect on data from myriad sources on a common timeline. The project is also working to develop and spread peer coaching practices to help transfer the culture and skills of self-tracking while mentoring individuals in how to self-assess their own situation and guide the process for themselves.