用遗传算法优化车辆在不平地形上的运动规划，提升越障能力。
A Genetic Approach to Gradient-Free Kinodynamic Planning in Uneven Terrains
- 基于遗传算法在固定窗口内优化轨迹，结合启发式变异保持控制可行。
- 相比MPPI方法，越障成本降低20%，路径长度相当。
- 适合复杂不平地形的无人车运动规划，尤其看重控制安全性的场景。
本文提出一种基于遗传算法的运动学动力学规划方法（GAKD），用于类车车辆在三角网格建模的不平地形中导航。该方法在固定长度的递推视野内，通过带有启发式变异的遗传算法优化轨迹，确保车辆控制量始终处于有效操作范围内。针对不平地形网格带来的法向量变化等挑战，GAKD提供了一种在复杂环境中实用的路径规划解决方案。与模型预测路径积分（MPPI）及log-MPPI方法的对比实验表明，GAKD在路径可通行性成本上最高提升20%，同时路径长度保持相近。结果验证了GAKD在提升车辆在复杂地形中导航性能方面的潜力。
原文摘要 · Abstract (English)
This paper proposes a genetic algorithm-based kinodynamic planning algorithm (GAKD) for car-like vehicles navigating uneven terrains modeled as triangular meshes. The algorithm's distinct feature is trajectory optimization over a fixed-length receding horizon using a genetic algorithm with heuristic-based mutation, ensuring the vehicle's controls remain within its valid operational range. By addressing challenges posed by uneven terrain meshes, such as changing face normals, GAKD offers a practical solution for path planning in complex environments. Comparative evaluations against Model Predictive Path Integral (MPPI) and log-MPPI methods show that GAKD achieves up to 20 percent improvement in traversability cost while maintaining comparable path length. These results demonstrate GAKD's potential in improving vehicle navigation on challenging terrains.
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