A method for robustness improvement of robot obstacle avoidance algorithm

  • Guanghua Zong*
  • , Luhua Deng
  • , Wei Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The robustness of obstacle avoidance algorithm is one of the important factors to successful applications of mobile robot systems. The sonar ring is used widely for autonomous mobile robot obstacle avoidance. This paper first analyzes the robustness of the existing obstacle avoidance algorithms based on sonar ring, indicates that the certainty grid method for obstacle representation is helpful to the robustness improvement of obstacle avoidance algorithms, but its effect is limited, it also has many disadvantages. By the simulation of two typical obstacle avoidance algorithms, the damage of interfered sonar data is revealed. Then the kinematics model of obstacle avoidance is built, Kalman filter which can restrain divergence is designed for interfered sonar data. Sonar data is used by obstacle avoidance algorithm after filtering. By the simulation contrast of the two obstacle avoidance algorithms, the effect of the Kalman filter for robustness improvement of obstacle avoidance algorithms is testified. Finally, the effect of the Kalman filter for eliminating noises in sonar data and for robustness improvement of obstacle avoidance algorithms is verified by experiments in two different situation.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006
Pages115-119
Number of pages5
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 - Kunming, China
Duration: 17 Dec 200620 Dec 2006

Publication series

Name2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006

Conference

Conference2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006
Country/TerritoryChina
CityKunming
Period17/12/0620/12/06

Keywords

  • Kalman filter
  • Obstacle avoidance algorithm
  • Robot
  • Robustness
  • Sonar

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