A novel obstacle avoidance method based on multi-information inflation map

Rupeng Yuan (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)
Fuhai Zhang (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)
Jiadi Qu (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)
Guozhi Li (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)
Yili Fu (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)

Industrial Robot

ISSN: 0143-991x

Publication date: 24 August 2019

Abstract

Purpose

This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map.

Design/methodology/approach

In this paper, the multi-information inflation map is introduced, which considers different information, including a two-dimensional grid map and a variety of sensor information. The static layer of the map is pre-processed at first. Then sensor inputs are added in different semantic layers. The processed information in semantic layers is used to update the static layer. The obstacle avoidance algorithm based on the multi-information inflation map is able to generate different avoidance paths for different kinds of obstacles, and the motion planning based on multi-information inflation map can track the global path and drive the robot.

Findings

The proposed method was implemented on a self-made mobile robot. Four experiments are conducted to verify the advantages of the proposed method. The first experiment is to demonstrate the advantages of the multi-information inflation map over the layered cost map. The second and third experiments verify the effectiveness of the obstacle avoidance path generation and motion planning. The fourth experiment comprehensively verifies that the obstacle avoidance algorithm is able to deal with different kinds of obstacles.

Originality/value

The multi-information inflation map proposed in this paper has better performance than the layered cost maps. As the static layer is pre-processed, the computational efficiency is higher. Sensor information is added in semantic layers with different cost attenuation coefficients. All layers are reset before next update. Therefore, the previous state will not affect the current situation. The obstacle avoidance and motion planning algorithm based on the multi-information inflation map can generate different paths for different obstacles and drive a robot safely and control the velocity according to different conditions.

Keywords

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61673134), the Natural Science Foundation of Heilongjiang Province of China (Grant No. LC2017022), and the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province of China (Grant No. LBH-Q17071).

Citation

Yuan, R., Zhang, F., Qu, J., Li, G. and Fu, Y. (2019), "A novel obstacle avoidance method based on multi-information inflation map", Industrial Robot, Vol. 47 No. 2, pp. 253-265. https://doi.org/10.1108/IR-05-2019-0114

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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