Mars Rover Sample Collection Path Planning Based on Improved Quantum Dung Beetle Algorithm

Existing Mars rover path planning methods still have room for optimization in terms of accuracy and efficiency, particularly in sample collection tasks that consider the rover's capacity limitations. Traditional algorithms are prone to local optima and exhibit low efficiency. To address these limitations, this study proposes an enhanced path planning scheme based on an improved quantum dung beetle algorithm. First, an energy consumption model for the Mars rover is established. Then, a nonlinear adaptive mechanism is introduced to adjust the quantum rotation angle, leveraging the superposition and entanglement characteristics of quantum computing to enhance the efficiency of the traditional dung beetle algorithm. Additionally, considering the rover's energy constraints and terrain safety risks, a Pareto front dynamic weight allocation strategy is incorporated to optimize the sample collection path under limited power conditions. The proposed approach is evaluated through extensive simulations, and the results demonstrate that it significantly improves both the accuracy and efficiency of path planning, providing an effective solution for Mars rover sampling missions.