A deep-network solution towards model-less obstacle avoidance

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

80 Scopus Citations
View graph of relations

Author(s)

  • Lei Tai
  • Shaohua Li
  • Ming Liu

Detail(s)

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2759-2764
Volume2016-November
ISBN (Print)9781509037629
Publication statusPublished - Oct 2016

Publication series

Name
Volume2016-November
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Title2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
LocationDaejeon Convention Center
PlaceKorea, Republic of
CityDaejeon
Period9 - 14 October 2016

Abstract

Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such as constructing local cost-maps) which could be regarded as low-level intelligence without any cognitive process. Recently, deep learning has made great breakthroughs in computer vision, especially for recognition and cognitive tasks. It takes advantage of the hierarchical models inspired by human brain structures. However, it is a fact that deep learning, up till now, has seldom been used for controlling and decision making. Inspired by the advantages of deep learning, we take indoor obstacle avoidance as example to show the effectiveness of a hierarchical structure that fuses a convolutional neural network (CNN) with a decision process. It is a highly compact network structure that takes raw depth images as input, and generates control commands as network output, by which a model-less obstacle avoidance behavior is achieved. We test our approach in real-world indoor environments. The new findings and results are reported at the end of the paper.

Citation Format(s)

A deep-network solution towards model-less obstacle avoidance. / Tai, Lei; Li, Shaohua; Liu, Ming.

IEEE International Conference on Intelligent Robots and Systems. Vol. 2016-November Institute of Electrical and Electronics Engineers Inc., 2016. p. 2759-2764.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)