JRM Vol.35 No.2 pp. 510-520
doi: 10.20965/jrm.2023.p0510


Investigation of Obstacle Prediction Network for Improving Home-Care Robot Navigation Performance

Mohamad Yani*,** ORCID Icon, Azhar Aulia Saputra**, Wei Hong Chin** ORCID Icon, and Naoyuki Kubota** ORCID Icon

*Department of Computer Engineering, Faculty of Electronics and Intelligent Industry Technology, Institut Teknologi Telkom Surabaya
Surabaya , Indonesia

**Department of Mechanical System Engineering, Faculty of System Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

February 2, 2022
February 9, 2023
April 20, 2023
safety, unseen obstacle, obstacle prediction networks
2D Lidar for unseen obstacles

2D Lidar for unseen obstacles

Home-care manipulation robot requires exploring and performing the navigation task safely to reach the grasping target and ensure human safety in the home environment. An indoor home environment has complex obstacles such as chairs, tables, and sports equipment, which make it difficult for robots that rely on 2D laser rangefinders to detect. On the other hand, the conventional approaches overcome the problem by using 3D LiDAR, RGB-D camera, or fusing sensor data. The convolutional neural network has shown promising results in dealing with unseen obstacles in navigation by predicting the unseen obstacle from 2D grid maps to perform collision avoidance using 2D laser rangefinders only. Thus, this paper investigated the predicted grid map from the obstacle prediction network result for improving indoor navigation performance using only 2D LiDAR measurement. This work was evaluated by combining the configuration of the various local planners, type of static obstacles, raw map, and predicted map. Our investigation demonstrated that using the predicted grid map enabled all the local planners to achieve a better collision-free path by using the 2D laser rangefinders only rather than the RGB-D camera with 2D laser rangefinders with a raw map. This advanced investigation considers that the predicted map is potentially helpful for future work in the learning-based local navigation system.

Cite this article as:
M. Yani, A. Saputra, W. Chin, and N. Kubota, “Investigation of Obstacle Prediction Network for Improving Home-Care Robot Navigation Performance,” J. Robot. Mechatron., Vol.35 No.2, pp. 510-520, 2023.
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Last updated on Jun. 07, 2023