Energy Efficient Path Planning for Indoor Wheeled Mobile Robots
Sponsor
The authors would like to acknowledge the partial supports provided by the National Natural Science Foundation of China (key project) under the contract number 61833002 and the National Natural Science Foundations of China under the contract number 51275077.
Published In
2020 Global Reliability and Prognostics and Health Management (phm-Shanghai)
Document Type
Citation
Publication Date
2020
Abstract
There has been an increased interest in the optimal robot path planning study. However, the optimal path found by the traditional path planning methods only considers distance and time constraints. Thus, it is not necessarily the most energy efficient path. In order to make robots perform more tasks under insufficient energy supply efficiently, reducing energy consumption has become essential in the path planning. In this paper, we proposed a novel path smoothing method that overcomes the deficiencies (e.g., some redundant inflection points in path.) of the traditional A* algorithm. We designed an energy-based adjacency matrix to represent the environmental information in grid map. Then we added a new energy-related criterion based on an adjacency matrix to the cost function of the A* algorithm for path planning. The simulation results demonstrated that the proposed method is more energy efficient than the existing A* algorithm. Moreover, the method realizes a good trade-off between preserving energy and not extending too much the path length. With such advantages, the proposed methods can help indoor wheeled mobile robots or automated guided vehicles (AGVs) to complete path planning tasks with limited power.
Rights
© Copyright 2020 IEEE
Locate the Document
DOI
10.1109/PHM-Shanghai49105.2020.9280948
Persistent Identifier
https://archives.pdx.edu/ds/psu/34642
Publisher
IEEE
Citation Details
J. Liu, Z. Li, L. -P. He and W. Shi, "Energy Efficient Path Planning for Indoor Wheeled Mobile Robots," 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), Shanghai, 2020, pp. 1-7, doi: 10.1109/PHM-Shanghai49105.2020.9280948.