Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the r...

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Bibliographic Details
Main Authors: Mingshan Chi, Yufeng Yao, Yaxin Liu, Ming Zhong
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/9/8/1535
Description
Summary:In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.
ISSN:2076-3417