Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation

  • Sheng Fei Chik School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, MALAYSIA
  • Che Fai Yeong Centre for Artificial Intelligence and Robotics, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, MALAYSIA
  • Lee Ming Su School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, MALAYSIA
  • Thol Yong Lim Malaysia Japan Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, 54100, MALAYSIA
  • Fend Duan Department of Automation, College of Computer and Control Engineering, Nankai University, Tianjin, 300071, China
  • Jun Hua Chin DF Automation and Robotics Sdn. Bhd, Johor Bahru, 81300, Malaysia
Keywords: Neural-network, proxemics, adaptive, costmap, human-aware

Abstract

In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency. Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap. Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance. However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity). This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other. To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters. The proposed proxemic-costmap was developed by learning a neural-network model using real human state data. A total of three human scenarios were used for data collection. The data were collected by tracking the humans in video recordings. After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states. Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort.

 

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Published
05-09-2019
How to Cite
Chik, S. F., Yeong, C. F., Su, L. M., Lim, T. Y., Duan, F., & Chin, J. H. (2019). Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation. International Journal of Integrated Engineering, 11(4). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4190