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Appl. Sci. 2019, 9(8), 1535; https://doi.org/10.3390/app9081535

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

1,2, 1,3, 1,3 and 1,3,*
1
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
2
Department of Mechanical Engineering, Harbin University of Science and Technology Rongcheng Campus, Rongcheng 264300, China
3
Industrial Research Institute of Robotics and Intelligent Equipment, Weihai 264209, China
*
Author to whom correspondence should be addressed.
Received: 12 March 2019 / Revised: 10 April 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Human Friendly Robotics)
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Abstract

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. View Full-Text
Keywords: service robots; dynamic motion primitives (DMPs); dynamic potential field (DPF); obstacle avoidance service robots; dynamic motion primitives (DMPs); dynamic potential field (DPF); obstacle avoidance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chi, M.; Yao, Y.; Liu, Y.; Zhong, M. Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields. Appl. Sci. 2019, 9, 1535.

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