Journal of Drive and Control (드라이브 ㆍ 컨트롤)

The Korean Society for Fluid Power and Construction Equipment (유공압건설기계학회)
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Route Optimization for Energy-Efficient Path Planning in Smart Factory Autonomous Mobile Robot

스마트 팩토리 모빌리티 에너지 효율을 위한 경로 최적화에 관한 연구

  • Dong Hui Eom (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Dong Wook Cho (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Seong Ju Kim (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Sang Hyeon Park (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Sung Ho Hwang (Department of Mechanical Engineering, Sungkyunkwan University)
  • Received : 2023.12.27
  • Accepted : 2024.02.23
  • Published : 2024.03.01
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Abstract

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A*, and RRT*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

Keywords

Acknowledgement

이 논문은 2024년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원(P0017120, 2024년 산업혁신인재성장지원사업)과 2024년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원에 의한 연구임(20014983)

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