NDLI: Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot

Content Provider	Springer Nature Link
Author	Li, Jianghao Li, Zhenbo Chen, Jiapin
Copyright Year	2009
Abstract	This paper introduces the path planning of a 1 cm3 mobile microrobot that is designed for microassembly in a microfactory. Since the conventional path planning method can not achieve high microassembly positioning accuracy, a supervised learning assisted reinforcement learning (SL-RL) method has been developed. In this mixed learning method, the reinforcement learning (RL) is used to search a movement path in the normal learning area. But when the microrobot moves into the buffer area, the supervised learning (SL) is employed to prevent it from moving out of the boundary. The SL-RL uses a gradient descent algorithm based on uniform grid tile coding under SARSA(λ) to handle the large learning state space. In addition to the uniform grid tile model, two irregular tile models called an uneven grid tile model and a cobweb tile model are designed to partition the microrobot state space. The main conclusions demonstrated by simulations are as follows: First, the SL-RL method achieves higher positioning accuracy than the conventional path planning method; second, the SL-RL method achieves higher positioning accuracy and learning efficiency than the single RL method; and third, the irregular tile models show higher learning efficiency than the uniform tile model. The cobweb tile model performs especially well.
Starting Page	211
Ending Page	225
Page Count	15
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	34
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2009-09-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mobile microrobot Path planning Reinforcement learning Supervised learning Microassembly positioning Artificial Intelligence (incl. Robotics) Manufacturing, Machines, Tools Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot

Online exploratory behavior acquisition model based on reinforcement learning

Learning Generalized Policies from Planning Examples Using Concept Languages

Bayes-adaptive hierarchical MDPs

Semi-supervised Bayesian ARTMAP

A layered approach to learning coordination knowledge in multiagent environments

Monte-Carlo tree search for Bayesian reinforcement learning

Approximate planning for bayesian hierarchical reinforcement learning

RTTES: Real-time search in dynamic environments

Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot

Similar Documents

XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot

Online exploratory behavior acquisition model based on reinforcement learning

Learning Generalized Policies from Planning Examples Using Concept Languages

Bayes-adaptive hierarchical MDPs

Semi-supervised Bayesian ARTMAP

A layered approach to learning coordination knowledge in multiagent environments

Monte-Carlo tree search for Bayesian reinforcement learning

Approximate planning for bayesian hierarchical reinforcement learning

RTTES: Real-time search in dynamic environments

Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot