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An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

Author

Listed:
  • Ricardo Paul Urvina

    (Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Antofagasta 1249004, Chile)

  • César Leonardo Guevara

    (Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN2 2LG, UK)

  • Juan Pablo Vásconez

    (Energy Transformation Center, Faculty of Engineering, Universidad Andrés Bello, Santiago 7500000, Chile)

  • Alvaro Javier Prado

    (Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Antofagasta 1249004, Chile)

Abstract

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree ( IRRT * ) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT * planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT * algorithm outperformed RRT and RRT * with 96.1% and 97.6% smoother paths, respectively. The IRRT * also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Suggested Citation

  • Ricardo Paul Urvina & César Leonardo Guevara & Juan Pablo Vásconez & Alvaro Javier Prado, 2024. "An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints," Agriculture, MDPI, vol. 14(8), pages 1-26, July.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1206-:d:1440818
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    References listed on IDEAS

    as
    1. Haoling Ren & Jiangdong Wu & Tianliang Lin & Yu Yao & Chang Liu, 2023. "Research on an Intelligent Agricultural Machinery Unmanned Driving System," Agriculture, MDPI, vol. 13(10), pages 1-19, September.
    2. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
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