Path Planning for Automation of Surgery Robot based on Probabilistic Roadmap and Reinforcement Learning

Donghoon Baek, Minho Hwang, Hansoul Kim, Dong Soo Kwon

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

35 Scopus citations

Abstract

Laparoscopic robotic surgery is a new surgical method performed by inserting several surgical tools and a laparoscope through an umbilical incision [12]. Compared with conventional laparoscopic surgery minimizes patient pain with minimally invasive surgery and has many advantages in terms of beauty. However, medical doctor's fatigue due to repetitive operations such as tissue resection and suturing still remains a problem to be improved. To solve this problem, there are a lot of automation researches on surgical robots [1], [7]-[10]. Especially in cutting automaton, for high accuracy, optimal path planning is essential factor. Probabilistic Roadmap (PRM) is a popular method for path planning. It creates path from static environment to desired point without collision. However, this does not show great performance in a dynamic environment. Reinforcement Learning (RL) shows strong performance in an unspecified probabilistic environment and it is widely applied to robot motion learning because learning data is not needed before [4]. In this paper, we suggest a collision avoidance path planning for automation of surgery robot by using PRM and RL in dynamic situation. We found the collision avoidance path through PRM and RL, and used mapping algorithm of coordination system from pixel to world coordination and transformed the coordination system from cartesian space to joint space using inverse kinematics. Finally, we apply it to the APOLLON laparoscopic surgery robotic system developed by KAIST in V-rep simulator. As a result, we confirmed a possible of collision avoidance path planning for automation of resection task for surgery robot.

Original languageEnglish
Title of host publication2018 15th International Conference on Ubiquitous Robots, UR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages342-347
Number of pages6
ISBN (Print)9781538663349
DOIs
StatePublished - 20 Aug 2018
Event15th International Conference on Ubiquitous Robots, UR 2018 - Honolulu, United States
Duration: 27 Jun 201830 Jun 2018

Publication series

Name2018 15th International Conference on Ubiquitous Robots, UR 2018

Conference

Conference15th International Conference on Ubiquitous Robots, UR 2018
Country/TerritoryUnited States
CityHonolulu
Period27/06/1830/06/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • APOLLON system
  • Automation of resection
  • Laparoscopic surgery robot
  • Probabilistic Roadmap
  • Reinforcement Learning

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