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

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...

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Bibliographic Details
Published in:Agriculture (Basel) 2024-08, Vol.14 (8), p.1206
Main Authors: Urvina, Ricardo Paul, Guevara, César Leonardo, Vásconez, Juan Pablo, Prado, Alvaro Javier
Format: Article
Language:English
Subjects:
Agricultural production
Agricultural societies
Agriculture
Algorithms
Artificial intelligence
capacitated vehicle routing
Constraints
Crop yield
Farms
Fruits
Harvest
Harvesting
harvesting tasks
Kinematics
landscapes
Local planning
Obstacle avoidance
Orchards
Path planning
Planning
precision agriculture
Precision farming
rapidly-exploring random tree
Robot dynamics
Robotics
Robots
route and path planning
skid–steer mobile robot
Strategy
Task complexity
Task scheduling
Terrain
Traveling salesman problem
Vehicle routing
Vehicles
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Summary: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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture14081206