/Experimental Results for Sensor Based Planning for Hyper-redundant Manipulators

Experimental Results for Sensor Based Planning for Hyper-redundant Manipulators

N. Takanashi, Howie Choset and J. Burdick
Conference Paper, Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93), October, 1993

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

This paper presents a dual resolution local sensor based planning method for hyper redundant robot mechanisms. Two classes of sensor feedback control methods working at different sampling rates and different spatial resolutions are considered – full shape modification (FSM) and partial shape modification (PSM). FSM and PSM cooperate to utilize a mechanism’s hyper redundancy to enable both local obstacle avoidance and end effector placement in real time. These methods have been implemented on a thirty degree of freedom hyper redundant manipulator which has eleven ultrasonic distance measurement sensors and twenty infrared proximity sensors. The implementation of these algorithms in a dual CPU real time control computer, an innovative sensor bus architecture, and a novel graphical control interface are described. Experimental results obtained using this test bed show the efficacy of the proposed method.

BibTeX Reference
@conference{Takanashi-1993-13577,
author = {N. Takanashi and Howie Choset and J. Burdick},
title = {Experimental Results for Sensor Based Planning for Hyper-redundant Manipulators},
booktitle = {Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93)},
year = {1993},
month = {October},
}
2017-09-13T10:51:38+00:00