NDLI: Actuator networks for navigating an unmonitored mobile robot

Content Provider	IEEE Xplore Digital Library
Author	Schiff, J. Kulkarni, A. Bazo, D. Duindamx, V. Alterovitz, R. Dezhen Song Goldberg, K.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, CA (Schiff, J.)
Abstract	Building on recent work in sensor-actuator networks and distributed manipulation, we consider the use of pure actuator networks for localization-free robotic navigation. We show how an actuator network can be used to guide an unobserved robot to a desired location in space and introduce an algorithm to calculate optimal actuation patterns for such a network. Sets of actuators are sequentially activated to induce a series of static potential fields that robustly drive the robot from a start to an end location under movement uncertainty. Our algorithm constructs a roadmap with probability-weighted edges based on motion uncertainty models and identifies an actuation pattern that maximizes the probability of successfully guiding the robot to its goal. Simulations of the algorithm show that an actuator network can robustly guide robots with various uncertainty models through a two-dimensional space. We experiment with additive Gaussian Cartesian motion uncertainty models and additive Gaussian polar models. Motion randomly chosen destinations within the convex hull of a 10-actuator network succeeds with with up to 93.4% probability. For n actuators, and m samples per transition edge in our roadmap, our runtime is $O(mn^{6}).$
Starting Page	53
Ending Page	60
File Size	1605647
Page Count	8
File Format	PDF
ISBN	9781424420223
DOI	10.1109/COASE.2008.4626500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges USA Councils Automation Conferences Motion Planning Sensor Networks Actuator Networks Robotic Navigation Potential Fields
Content Type	Text
Resource Type	Article

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