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This paper presents a novel feature-based technique for path optimization problems, in which the performance index is defined to minimize the energy consumption of a rover with consideration of terrain, kinematic, and dynamic constraints. The proposed method estimates rover energy consumption by discretizing a path and by extracting statistical data for fast calculation of the performance index. The concepts of grouped data and data discretization techniques are used to analyze the energy-related data obtained from the search environment. The method improves runtime computation by statistically calculating the energy consumption of a rover for a defined path, rather than solving the dynamic equations of the rover. This technique is computationally more efficient than other energy optimization approaches when it estimates rover energy consumption with sufficient accuracy. The Genetic Algorithm (GA) solver is integrated to the approach to illustrate the efficiency of the algorithm. Additionally, a hardware-in-the-loop (HIL) simulation is developed for the validation of the rover's power flow as it traverses through the optimal path by incorporating rover hardware components within real-time simulation.