This research introduces a novel pathfinding framework designed for autonomous robots navigating diverse terrains. The framework leverages advanced Monte Carlo simulations to evaluate multiple potential paths dynamically under varying environmental conditions. Users can choose specific goals from Pareto optimal solutions, such as energy efficiency or safety. Our findings underscore the profound influence of path composition on the performance metrics of autonomous navigation systems. For instance, routes predominantly comprising unpaved sections culminating in a paved segment offer a balance between energy consumption and navigational risk. In contrast, fully paved routes escalate energy consumption but significantly mitigate navigational risk. Rigorous experimental validation demonstrates that our framework amplifies autonomous robots’ operational range and safety across different terrains. This research propels the field of robotic navigation by providing a versatile decision-support tool, empowering users to make informed decisions that align with their operational strategies. Ultimately, our framework holds the potential to bolster the reliability and performance of autonomous robots, paving the way for safer, more efficient, and sustainable robotic operations.