The International Journal of Robotics Research current issue

Annual Editorial

Hollerbach, J. — Mon, 26 Oct 2009 09:41:35 PDT

Editorial: Special Issue on the 13th International Symposium on Robotics Research, 2007

Kaneko, M., Nakamura, Y. — Mon, 26 Oct 2009 09:41:35 PDT

Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

Mon, 26 Oct 2009 09:41:35 PDT

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

How Should Microrobots Swim?

Mon, 26 Oct 2009 09:41:35 PDT

Microrobots have the potential to dramatically change many aspects of medicine by navigating through bodily fluids to perform targeted diagnosis and therapy. Researchers have proposed numerous micro-robotic swimming methods, with the vast majority utilizing magnetic fields to wirelessly power and control the microrobot. In this paper, we compare three promising methods of microrobot swimming (using magnetic fields to rotate helical propellers that mimic bacterial flagella, using magnetic fields to oscillate a magnetic head with a rigidly attached elastic tail, and pulling directly with magnetic field gradients) considering practical hardware limitations in the generation of magnetic fields. We find that helical propellers and elastic tails have very comparable performance, and they generally become more desirable than gradient pulling as size decreases and as distance from the magnetic-field-generation source increases. We provide a discussion of why helical propellers are likely the best overall choice for in vivo applications.

The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance

Prentice, S., Roy, N. — Mon, 26 Oct 2009 09:41:35 PDT

When a mobile agent does not know its position perfectly, incorporating the predicted uncertainty of future position estimates into the planning process can lead to substantially better motion performance. However, planning in the space of probabilistic position estimates, or belief space, can incur a substantial computational cost. In this paper, we show that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix. This factored form allows several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning. We give a belief-space variant of the probabilistic roadmap algorithm called the belief roadmap (BRM) and show that the BRM can compute plans substantially faster than conventional belief space planning. We conclude with performance results for an agent using ultra-wide bandwidth radio beacons to localize and show that we can efficiently generate plans that avoid failures due to loss of accurate position estimation.

A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle

Bajracharya, M., Moghaddam, B., Howard, A., Brennan, S., Matthies, L. H. — Mon, 26 Oct 2009 09:41:35 PDT

In this paper we describe a fully integrated system for detecting, localizing, and tracking pedestrians from a moving vehicle. The system can reliably detect upright pedestrians to a range of 40 m in lightly cluttered urban environments. The system uses range data from stereo vision to segment the scene into regions of interest, from which shape features are extracted and used to classify pedestrians. The regions are tracked using shape and appearance features. Tracking is used to temporally filter classifications to improve performance and to estimate the velocity of pedestrians for use in path planning. The end-to-end system runs at 5 Hz on 1,024 x 768 imagery using a standard 2.4 GHz Intel Core 2 Quad processor, and has been integrated and tested on multiple ground vehicles and environments. We show performance on a diverse set of datasets with groundtruth in outdoor environments with varying degrees of pedestrian density and clutter. In highly cluttered urban environments, the detection rates are on a par with state-of-the-art but significantly slower systems.

Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion

Vasquez, D., Fraichard, T., Laugier, C. — Mon, 26 Oct 2009 09:41:35 PDT

Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally, and in parallel with prediction. Our work is based on a novel extension to hidden Markov models, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been evaluated using synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques.

Requirements for Safe Robots: Measurements, Analysis and New Insights

Haddadin, S., Albu-Schaffer, A., Hirzinger, G. — Mon, 26 Oct 2009 09:41:35 PDT

Physical human—robot interaction and cooperation has become a topic of increasing importance and of major focus in robotics research. An essential requirement of a robot designed for high mobility and direct interaction with human users or uncertain environments is that it must in no case pose a threat to the human. Until recently, quite a few attempts were made to investigate real-world threats via collision tests and use the outcome to considerably improve safety during physical human—robot interaction. In this paper, we give an overview of our systematic evaluation of safety in human—robot interaction, covering various aspects of the most significant injury mechanisms. In order to quantify the potential injury risk emanating from such a manipulator, impact tests with the DLR-Lightweight Robot III were carried out using standard automobile crash test facilities at the German Automobile Club (ADAC). Based on these tests, several industrial robots of different weight have been evaluated and the influence of the robot mass and velocity have been investigated. The evaluated non-constrained impacts would only partially capture the nature of human—robot safety. A possibly constrained environment and its effect on the resulting human injuries are discussed and evaluated from different perspectives. As well as such impact tests and simulations, we have analyzed the problem of the quasi-static constrained impact, which could pose a serious threat to the human even for low-inertia robots under certain circumstances. Finally, possible injuries relevant in robotics are summarized and systematically classified.