Obstacle avoidance for robotic excavators using a recurrent neural network

Hyongju Park, Sanghak Le, Baeksuk Chu, Daehie Hong

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

2 Citations (Scopus)

Abstract

In this paper, we present a recurrent neural network to resolve the obstacle avoidance problem of excavators. The conventional pseudo-inverse formulation requires excessive computation time for on-line or real time application. To effectively accomplish following goals: excavation task execution, joint limit control, and obstacle avoidance at the same time, conventional Newton-iteration scheme was replaced by a recurrent neural network algorithm in this study. The recurrent neural network was implemented for better kinematics control of the excavator with obstacle avoidance capability. In automated excavation environments, potential dangers exist if a worker is within the workspace of the excavator. When an obstacle is detected by a sensor, accidents can be easily prevented by halting the excavation process using a simple fail-safe algorithm. However, it would be more desirable to handle the unforeseen obstacles intelligently on-line while continuing the excavation task instead of stopping. For excavators, an obstacle can be classified into two categories. The first category includes obstacles on the ground such as trees, workers, and buildings. The second category of obstacles includes underground obstructions such as tree roots, boulders and etc. This paper focuses on the first category of these obstacles and was written to meet the emphasis requirements of avoiding obstacles on the ground for the excavator.

Original languageEnglish
Title of host publicationICSMA 2008 - International Conference on Smart Manufacturing Application
Pages585-590
Number of pages6
DOIs
Publication statusPublished - 2008
EventInternational Conference on Smart Manufacturing Application, ICSMA 2008 - Gyeonggi-do, Korea, Republic of
Duration: 2008 Apr 92008 Apr 11

Publication series

NameICSMA 2008 - International Conference on Smart Manufacturing Application

Other

OtherInternational Conference on Smart Manufacturing Application, ICSMA 2008
CountryKorea, Republic of
CityGyeonggi-do
Period08/4/908/4/11

Keywords

  • Collision free
  • Excavator
  • Joint limits
  • Obstacle avoidance
  • Recurrent neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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  • Cite this

    Park, H., Le, S., Chu, B., & Hong, D. (2008). Obstacle avoidance for robotic excavators using a recurrent neural network. In ICSMA 2008 - International Conference on Smart Manufacturing Application (pp. 585-590). [4505593] (ICSMA 2008 - International Conference on Smart Manufacturing Application). https://doi.org/10.1109/ICSMA.2008.4505593