NDLI: View-time based moving obstacle avoidance using stochastic prediction of obstacle motion

Content Provider	IEEE Xplore Digital Library
Author	Yun Seok Nam Bum Hee Lee Moon Sang Kim
Copyright Year	1996
Description	Author affiliation: Dept. of Control & Instrum. Eng., Seoul Nat. Univ., South Korea (Yun Seok Nam; Bum Hee Lee)
Abstract	This paper proposes a new motion planning method of a mobile robot avoiding moving obstacles. To avoid moving obstacles, the trajectories of the obstacles are predicted using a stochastic model of obstacle motion. The obstacle motion is modeled as a random walk process. The method plans robot motion by the unit of view-time and view-period. View-time is defined as the time instant at which the robot senses the obstacle position and velocity. View-period is defined as the time interval during which the robot performs sensing, predicting and planning for collision-free motion. From the position and velocity at a view-time, we predict the future position of the obstacle. The random walk process model of obstacle motion is used to calculate the probability density that the predicted position is reached during the view-period. From the probability density function of the predicted position, the probability that a position can be swept by the obstacle during the view-period is calculated. Then artificial potential is assigned at every position by considering the probability. The force induced by the artificial potential field repels the robot away from the probable obstacle trajectory. This method is a look ahead scheme, and effective for moving obstacle avoidance. This method is applied to collision-free motion planning for a mobile robot in a dynamic and unknown environment with several moving and stationary obstacles.
Starting Page	1081
Ending Page	1086
File Size	706077
Page Count	6
File Format	PDF
ISBN	0780329880
ISSN	10504729
DOI	10.1109/ROBOT.1996.506852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-04-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Motion planning Robot sensing systems Probability Predictive models Trajectory Robot motion Hidden Markov models Mobile robots Orbital robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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