Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments with diverse obstacle configurations. However, in practice, these methods often require many iterations to obtain high-quality solutions. This paper proposes Convex-Neural RRT*, an enhanced RRT* variant that incorporates neural guidance to predict informative waypoint regions near high-quality paths. Convex candidate regions are extracted from these predictions, enabling the planner to concentrate exploration on geometrically relevant areas while preserving global exploration. The proposed algorithm is evaluated against Neural RRT*, Neural Informed RRT*, classical RRT*, and LTA* across three environment types and 18 benchmark maps. Experimental results show that Convex-Neural RRT* reduces computation time by 30-75% compared to neural-guided variants and up to 88-98% relative to LTA*, while achieving an average path length reduction of approximately 5% compared to classical RRT*, with larger improvements observed in complex environments. The method also maintains an overall success rate above 99% across varying obstacle densities. These findings indicate that convex-guided neural sampling provides an effective balance between computational efficiency and solution quality, supporting its applicability to time-sensitive robotic navigation tasks.

翻译：基于采样的机器人路径规划算法在不同障碍物配置的环境中具有概率完备性和强经验收敛特性。然而在实际应用中，这些方法通常需要大量迭代才能获得高质量解。本文提出Convex-Neural RRT*，一种增强型RRT*变体，通过引入神经引导机制预测靠近高质量路径的信息性航点区域。从这些预测中提取凸候选区域，使规划器能够在保持全局探索的同时，将探索集中在几何相关区域。该算法在三种环境类型和18个基准地图中，与Neural RRT*、Neural Informed RRT*、经典RRT*及LTA*进行了对比评估。实验结果表明，与神经引导变体相比，Convex-Neural RRT*的计算时间减少30-75%；与LTA*相比减少高达88-98%；同时相较于经典RRT*，平均路径长度减少约5%，在复杂环境中改善更为显著。该方法在不同障碍物密度下保持99%以上的整体成功率。这些发现表明，凸引导神经采样方法能在计算效率与解质量之间实现有效平衡，支持其在时间敏感型机器人导航任务中的适用性。