A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.

A robust layered control system for a mobile robot

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Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.

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KinectFusion: Real-time dense surface mapping and tracking

Lightweight assistive manufacturing solutions: improving Australia’s manufacturing competitiveness

NASA is supposed to implement a sustainable and affordable human and robotic program to explore the solar system and beyond as it is the first goal of The Presidents Vision for U.S. Space Exploration. The robotic exploration across the solar system consists of exploring Jupiters moons, asteroids and other bodies to search for evidence of life, and to understand the history of the solar system. Trajectory generation for a robotic vehicle is an essential part of the total mission planning. To save energy by exploiting possible situation such as wind will assist a robotic explorer extend its life span and perform tasks more reliably. In this paper, we propose to utilize Nonlinear Trajectory Generation (NTG) methodology to generate 3D opportunistic trajectories for an Aerobot by exploiting wind. The Aerobot is dynamically controlled by three propellers which are respectively parallel to the local three Cartesian axes. Constraints for the Aerobot control are derived from Euler-Lagrange equations since the Aerobot satisfies with the Lagrange-D'Alembert principle. The new proposed Aerobot model takes the aerodynamics into account. The results show that NTG can take the advantage of wind profiles to save significant energy for the defined goal.

Opportunistic 3D trajectory generation for the JPL Aerobot with Nonlinear Trajectory Generation methodology

The Exploration Technology Development Program (ETDP) of the ESMD (NASA Exploration Systems Mission Directorate) has supported the evolution of a capability intended to systematize and quantify the relative priorities of advanced capabilities needed for lunar science in concert with human return to the moon. The product is a quantified list of sensitivities (percent change in mission productivity with respect to potential percent change in advanced capability). These sensitivities would then be folded with estimated achievable technology improvements to determine achievable mission improvement. Initial application of this approach is in support of the OSEWG (Optimizing Science and Exploration Working Group) surface science scenarios; a joint effort of the NASA Directorate Integration Office of ESMD and the NASA Science Mission Directorate.

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Determining Technology Priorities to Enhance Lunar Surface Science Mission Productivity

We are developing onboard planning and execution technologies to provide robust and opportunistic mission operations for a potential Titan aerobot. Aerobot have the potential for collecting a vast amount of high priority science data. However, to be effective, an aerobot must address several challenges including communication constraints, extended periods without contact with Earth, uncertain and changing environmental conditions, maneuverability constraints and potentially short-lived science opportunities. We are developing the AerOASIS system to develop and test technology to support autonomous science operations for a potential Titan Aerobot. The planning and execution component of AerOASIS is able to generate mission operations plans that achieve science and engineering objectives while respecting mission and resource constraints as well as adapting the plan to respond to new science opportunities. Our technology leverages prior work on the OASIS system for autonomous rover exploration. In this paper we describe how the OASIS planning component was adapted to address the unique challenges of a Titan Aerobot and we describe a field demonstration of the system with the JPL prototype aerobot.

Robust and Opportunistic Autonomous Science for a Potential Titan Aerobot

An initiative to enhance SME productivitythrough fit for purpose Information and Robotic technologies - The value of Lightweight Assistive Manufacturing Solutions

Alberto Elfes

Papers

Lightweight assistive manufacturing solutions: improving Australia’s manufacturing competitiveness

Opportunistic 3D trajectory generation for the JPL Aerobot with Nonlinear Trajectory Generation methodology

Determining Technology Priorities to Enhance Lunar Surface Science Mission Productivity

Robust and Opportunistic Autonomous Science for a Potential Titan Aerobot

An initiative to enhance SME productivitythrough fit for purpose Information and Robotic technologies - The value of Lightweight Assistive Manufacturing Solutions