Multi-Criteria Approach to Path Planning for Unmanned Tractors Considering Energy Constraints and Soil Compaction

Abstract

Autonomous agricultural vehicles operating under the Controlled Traffic Farming (CTF) paradigm face complex routing challenges when minimizing soil compaction, total mission time, and station placement under battery constraints. This paper introduces Multi-Objective Coordinated Autonomous Routing and Placement with Fixed Lanes (MO-CARP-FL), a novel multi-objective evolutionary algorithm designed to optimize the coordinated routing of homogeneous autonomous tractors over a predefined field traffic lane. The algorithm simultaneously addresses five conflicting objectives: minimizing soil compaction using a logarithmic saturation model, minimizing total route time, reducing the number of charging stations, preserving spatial coherence in assigned routes, and balancing workload among tractors. Chromosomes encode both routing and station placement decisions, and custom crossover and mutation operators preserve structural feasibility. A soil compaction model and energy-aware constraints are integrated into the evaluation function. Experimental simulations demonstrate that MO-CARP-FL produces environmentally sensitive routing plans while reducing field degradation. The proposed method is validated through CTF field scenarios, and its results are visualized to provide interpretable insights into route distribution, station usage, and soil impact. This work contributes to multi-objective optimization in agricultural logistics by addressing both environmental impact and operational efficiency in autonomous field operations.