提出动态曲率约束路径规划算法，提升复杂环境下的路径优化能力。
Dynamic Curvature Constrained Path Planning
- 基于动态曲率约束设计新算法，适应二维空间复杂环境
- 对比RRT与PRM，在路径平滑性与成功率上表现更优
- 适合机器人导航、自动驾驶等对路径质量要求高的场景
有效路径规划是机器人、物流等多个领域中的关键挑战。本研究聚焦于二维空间中动态曲率约束路径规划算法（DCCPPA）的开发与评估。该算法旨在应对受限环境，优化路径解并满足曲率约束。研究不仅开发了算法，还与两种经典路径规划方法——快速扩展随机树（RRT）和概率路图（PRM）进行了对比分析，揭示了不同算法在各类应用中的性能与适应性。结果表明，DCCPPA在二维空间中具备高度灵活性与实用性，展现了其在真实世界路径规划任务中的潜力。关键词：路径规划，PRM，RRT，最优路径，二维路径规划。
原文摘要 · Abstract (English)
Effective path planning is a pivotal challenge across various domains, from robotics to logistics and beyond. This research is centred on the development and evaluation of the Dynamic Curvature-Constrained Path Planning Algorithm (DCCPPA) within two dimensional space. DCCPPA is designed to navigate constrained environments, optimising path solutions while accommodating curvature constraints.The study goes beyond algorithm development and conducts a comparative analysis with two established path planning methodologies: Rapidly Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM). These comparisons provide insights into the performance and adaptability of path planning algorithms across a range of applications.This research underscores the versatility of DCCPPA as a path planning algorithm tailored for 2D space, demonstrating its potential for addressing real-world path planning challenges across various domains. Index Terms Path Planning, PRM, RRT, Optimal Path, 2D Path Planning.
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