Showing papers presented at "Field and Service Robotics in 2005"

The Berkeley Lower Extremity Exoskeleton.

Abstract: University of California, Berkeley, Berkeley, CA 94720 The iirst junctional/oat/-carrying ant! efll~r;.:e ticaffy lIUWl /Ol1/Ol/S exoskefetoll ww' demollstrated at the University o/Califomia, Berkeley, lilli/kill/: at tile llveragl' speed of 1.3 11I 1s (2. 9 mph) while carrY;lIg a 34 kg (75 lb) payload. Four jllllc/ame1lta/ tec/lll% gies associated with the Berkeley lower extremity exoskeleton were tack/ell durillg 'he course of This project. '/11ese JOllr (:ore technologies ;IIc:/ude the design of 'he exoskeh'lOll arc:hilecIlIre, control schemes, a body local (lreo network 10 host the cOIllml algorithm, and a series of ol/-board pOWl~r I/I/its 10 power the (/('fl/(/tor~', sensors, alld the cOlI/plllers. nlis paper gives all overview of olle of the COl/1m/ s('IJ(~mes. The al/aIY~'is here is till extension of the classical definition of the sensilivity fUllction of a system: the ability of a system to reject disfllrballces or the measure oj system robusilles.\·. 'J11e confrol algorithm de veloped here increases the closed-loop system sellsitivity to its wearer\' forces (md torques withOllt allY measllremelll from Ihe !Vearer (s l/ch liS jorce, position, or electromyogram siglIal). 'J1le control method has little robusll/ess 10 parameter varia/ions and Iherefore requires a relatively good dynamic model of the systelll. The mule-offs betwl'CII havillg sensors to measure I/Unum variables and the lack of robustlless ro parameter variation ore describec/, [1l01: 10 ,1 I 15/ 1.2168 164J

Intentional Motion Online Learning and Prediction.

Abstract: Motion prediction for objects which are able to decide their trajectory on the basis of a planning or decision process (e.g. humans and robots) is a challenging problem. Most existing approaches operate in two stages: a) learning, which consists in observing the environment in order to identify and model possible motion patterns or plans and b) prediction, which uses the learned plans in order to predict future motions. In existing techniques, learning is performed on-line, hence, it is impossible to rene the existing knowledge on the basis of the new observations obtained during the prediction phase. This paper proposes a novel learning approach which represents plans as Hidden Markov Models and is able to estimate the parameters and structure of those models in an incremental fashion by using the Growing Neural Gas algorithm. Our experiments demonstrate that the technique works in real-time, is able to operate concurrently with prediction and that the resulting model produces long-term predictions.

Autonomous Helicopter Tracking and Localization Using a Self-Surveying Camera Array.

Abstract: A Self-surveying Camera Array (SSCA) is a vision-based local-area positioning system consisting of multiple ground-deployed cameras that are capable of self-surveying their extrinsic parameters while tracking and localizing a moving target. This paper presents the self-surveying algorithm being used to track a target helicopter in each camera frame and to localize the helicopter in an array-fixed frame. Three cameras are deployed independently in an arbitrary arrangement that allows each camera to view the helicopter's flight volume. The helicopter then flies an unplanned path that allows the cameras to calibrate the relative locations and orientations by utilizing a self-surveying algorithm that is extended from the well-known structure from motion algorithm and the bundle adjustment technique. This yields the cameras'extrinsic parameters enabling real-time helicopter positioning via triangulation. This paper also presents results from field trials, which verify the feasibility of the SSCA as a readily-deployable system applicable to helicopter tracking and localization. The results demonstrate that, compared to the differential GPS solution as true reference, the SSCA alone is capable of positioning the helicopter with meter-level accuracy. The SSCA has been integrated with onboard inertial sensors providing a reliable positioning system to enable successful autonomous hovering.

3D Position Tracking in Challenging Terrain

Abstract: The intent of this paper is to show how the accuracy of 3D position tracking can be improved by considering rover locomotion in rough terrain as a holistic problem. An appropriate locomotion concept endowed with a controller min- imizing slip improves the climbing performance, the accuracy of odometry and the signal/noise ratio of the onboard sensors. Sensor fusion involving an inertial mea- surement unit, 3D-Odometry, and visual motion estimation is presented. The exper- imental results show clearly how each sensor contributes to increase the accuracy of the 3D pose estimation in rough terrain.