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 ((Formula presented.)) 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 (Formula presented.) 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 (Formula presented.) algorithm outperformed RRT and (Formula presented.) with 96.1% and 97.6% smoother paths, respectively. The (Formula presented.) also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.
Original language | English |
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Article number | 1206 |
Journal | Agriculture (Switzerland) |
Volume | 14 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2024 |
Keywords
- agricultural machinery
- capacitated vehicle routing
- harvesting tasks
- rapidly-exploring random tree
- route and path planning
- skid–steer mobile robot
- terrain traversability constraints
- traveling salesman problem
ASJC Scopus subject areas
- Food Science
- Agronomy and Crop Science
- Plant Science