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

Motion of hexapod robots on inclined terrain is an important problem in legged robotics. This paper presents a controller that feeds in to an existing high-level controller with the goal of improving walking performance when ascending inclined terrain. The paper also proposes a new metric, the vertical cost of transport (VCoT), which is a modified form of the conventional energetic cost of transport. This is shown to be an e↵ective measure for comparing inclination ascension performance. The controller implements two behaviours, translating the body in the direction of increasing inclination and adjusting the foot placement during the walking cycle. This is evaluated using a hexapod robot on a range of inclinations, surfaces and gaits. The results show that the controller improves terrain ascension performance with respect to vertical cost of transport, static stability, foot slip and force distribution and identifies the inclination that results in the most e cient ascension of terrain for a given platform.

Steep Terrain Ascension Controller for Hexapod Robots

The productivity of scientific exploration of the Moon and Mars has been significantly improved through the mobility of roving vehicles (rovers) since these vehicles allow scientists to conduct operations well beyond the immediate vicinity of the landing area. This paper reports on a quantitative approach developed to evaluate the productivity of alternative human and robot work-system alternatives for a lunar science mission. A graph-search approach for task planning was used for assigning human and robotic work systems to scientific tasks in order to evaluate the productivity of different mobility options. The results were used to identify the benefits and costs of alternative rover combinations in order to establish guidelines for the roles of the different vehicle types. Pressurized rovers displayed advantages over unpressurized rovers due to enhanced range and duration yielding more science productivity. Multiple pressurized rovers were found to be more productive than multiple unpressurized rovers.

Mobility productivity impacts on selection of lunar exploration architectures

Recently developed materials and technology make it possible to operate a network of balloons to perform as effectively as a network of satellites for certain communication objectives. Balloons are the only unmanned air vehicle with a specific operation regulation that allows for its use even in the proximity of aircraft routes. We are concerned with the use of a network of balloons to operate a communication platform in high altitude (60 to 100 thousand feet). Such a solution could perform certain tasks as well as a network of satellites, but with a fraction of the investment. In this paper we discuss the use of such network for communication support in certain areas where many off-shore oil fields are located in Brazil. We comment on the most relevant technical and economic aspects of the feasibility of such network. In particular, we discuss: air-space security; winds forecast and choice of best altitude for each balloon to be launched; recovery logistics and optimization of trade-offs between cost and autonomy.

Alberto Elfes

Papers

Steep Terrain Ascension Controller for Hexapod Robots

Mobility productivity impacts on selection of lunar exploration architectures

Operating a Network of Balloons Instead of Satellites

Introduction to the Special Issue on Robotics and Computer Vision

Control, planning, and architecture