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Comparative Analysis of Three Obstacle Detection and Avoidance Algorithms for a Compact Differential Drive Robot I N V-Rep

  • Chika Yinka-Banjo
  • Obawole Daniel
  • Sanjay MisraEmail author
  • Oluranti Jonathan
  • Hector Florez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1051)

Abstract

The aim of this research is to build a compact differential drive robot using the Virtual Robotics Experimentation Platform. Sensors are embedded in the Pioneer 3-dx mobile robot to provide necessary data from the real world to the robot. The main purpose of the mobile robot is its ability to arrive at a given destination (goal) precisely and astutely, hence, significantly reducing the risk of human mistakes. Many existing algorithms like obstacle detection, lane detection is combined to provide the essential and basic control functionalities to the car. The mobile robot controller model runs on a series of benchmark tasks, and its performance is compared to conventional controllers. During the scope of this project, comparisons between different algorithms, hardware and tools have been made to choose the best-fit for the project. The results are obstacle detection algorithms and a terrain handling feature, that works very well in simulations and real-life situations. The major tailbacks during the development of this project were limitations caused by low hardware computational power, the presence of stronger processors would exponentially increase the throughput and consequently improve the accuracy of the scene objects and the obstacle detection algorithms.

Keywords

Bug Pioneer 3-DX VREP Sonar API OS Scene 

Notes

Acknowledgement

The authors gratefully acknowledge the support of African Institute for Mathematical Sciences (AIMS), Alumni small research grant (AASRG), the Organisation for Women in Science for the Developing World (OWSD), and L’oreal-Unesco for Women in Science.

The authors of this research also appreciate the immense contribution of Covenant University Centre for Research, Innovation, and Discovery (CUCRID) for its support for this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of LagosLagosNigeria
  2. 2.Covenant UniversityOtaNigeria
  3. 3.Universidad Distrital Francisco Jose de Caldas BogotaBogotaColombia

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