Obstacle-Avoidance Algorithm Using Deep Learning Based on RGBD Images and Robot Orientation

Ashraf Saleem, Khadija Al Jabri, Ahmed Al Maashri, Waleed Al Maawali, Mostefa Mesbah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inspired by the advantages of the hierarchical feature extraction of deep learning, this work investigates the development of a Convolutional Neural Network (CNN) algorithm to solve the problem of the mobile robot obstacle avoidance in an indoor environment. The algorithm takes raw images and robot orientation as input and generates control commands as network output. Control commands include go-straight-forward, turn-full-left, turn-half-left, turn-full-right, and turn-half-right. A dataset compiled using depth images (RGBD) and robot orientation data obtained by an Inertial Measurement Unit (IMU). In addition, the performance of the algorithm in terms of training options, hyperparameters, and output precision is evaluated and recommendations are provided accordingly. The final results show that the accuracy can be improved by including the robot orientation in the dataset, increasing the size of data, and tuning the network's hyperparameters. The CNN algorithm has shown great potential to get high path classification accuracy for obstacle avoidance for mobile robots.

Original languageEnglish
Title of host publication2020 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages268-272
Number of pages5
ISBN (Electronic)9781728167886
DOIs
Publication statusPublished - Apr 1 2020
Event7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 - Antalya, Turkey
Duration: Apr 14 2020Apr 16 2020

Publication series

Name2020 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020

Conference

Conference7th International Conference on Electrical and Electronics Engineering, ICEEE 2020
CountryTurkey
CityAntalya
Period4/14/204/16/20

Keywords

  • computer vision
  • deep learning
  • mobile robot
  • obstacle avoidance

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Hardware and Architecture
  • Signal Processing

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