Energy-aware bi-objective optimization for cooperative UGV–UAV Mars exploration

This study presents a bi-objective optimization framework to evaluate the cost-effectiveness of a cooperative UGV–UAV system for Mars exploration. The framework models trade-offs between mission effectiveness, defined by UAV image quality and coverage and UGV sampling capability, and resource usage, captured through an integrated energy-aware cost model. Four multi-objective optimization algorithms—NSGA-II, R-NSGA-II, MOEA/D, and SMSEMOA—are employed to generate Pareto-optimal solutions. SMSEMOA consistently outperforms the others in convergence and diversity, particularly under rigid panel conditions, and is statistically superior to MOEA/D. Flexible configurations, especially the 4-flexible panel, further enhance performance by achieving the lowest IGD and smallest spacing metric, indicating superior convergence and uniform Pareto distribution compared to 3-flexible and rigid types. Sensitivity analysis identifies battery weight as the dominant design variable, while cost, FPS, FOV, and solar cell area exhibit moderate, context-specific effects. Finally, IQR- and KDE-based range estimators are introduced to support robust and interpretable design decisions. The proposed framework provides quantitative insight for building energy-efficient, cost-aware, and adaptable robotic systems under planetary mission constraints.