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역기구학과 강화 학습을 활용한 리드 클라이밍 루트 파인딩 시뮬레이션 개발

Development of a Lead Climbing Route-Finding Simulation Using Inverse Kinematics and Reinforcement Learning

  • 노승현 (성결대학교 미디어소프트웨어학과 XICOM LAB) ;
  • 진성아 (성결대학교 미디어소프트웨어학과 XICOM LAB)
  • 투고 : 2024.08.12
  • Accepted : 2024.11.01
  • Published : 2024.11.30

Abstract

본 연구는 올림픽 정식 종목 중 하나인 리드 클라이밍에 초점을 맞춰서 루트 파인딩 시뮬레이션을 개발하는것을 목표로 한다. Unity를 활용하여 장면에 오브젝트를 배치하고 변수들을 정의하여 클라이밍 환경을 구성하고, 클라이밍 자세에 따른 상태를 생성하여 클라이머 모델의 관절 위치를 FABRIK 알고리즘으로 계산하였다. 그리고 ML-Agents를 활용하여 벡터 관측 선택과 이산적인 행동을 정의하고, CCW 알고리즘을 이용한 안정적인 자세를 정의하여 행동 마스킹과 에이전트의 행동 선택에 따른 처리 함수를 구현하였다. 모의실험 결과, 탑 홀드까지 루트 파인딩이 가능함을 확인하였고, 이러한 시뮬레이션이 리드 클라이밍 훈련을 진행하는 선수들에게 많은 도움을 줄 것으로 기대된다.

This study aims to develop a route-finding simulation focused on lead climbing, an official Olympic discipline. Objects were strategically using Unity placed and variables defined to construct a realistic climbing environment. Various climbing postures were generated, and the joint positions of the climber model were calculated using the FABRIK algorithm. ML-Agents were utilized to define vector observations and discrete actions, with stable postures determined using the CCW algorithm. Functions for behavior masking and agent action selection were implemented. Simulation results confirmed the feasibility of route-finding to the top hold, showing promise in enhancing training for lead climbers.

Keywords

References

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