In unstructured environments like parking lots or construction sites, due to the large search-space and kinodynamic constraints of the vehicle, it is challenging to achieve real-time planning. Several state-of-the-art planners utilize heuristic search-based algorithms. However, they heavily rely on the quality of the single heuristic function, used to guide the search. Therefore, they are not capable to achieve reasonable computational performance, resulting in unnecessary delays in the response of the vehicle. In this work, we are adopting a Multi-Heuristic Search approach, that enables the use of multiple heuristic functions and their individual advantages to capture different complexities of a given search space. Based on our knowledge, this approach was not used previously for this problem. For this purpose, multiple admissible and non-admissible heuristic functions are defined, the original Multi-Heuristic A* Search was extended for bidirectional use and dealing with hybrid continuous-discrete search space, and a mechanism for adapting scale of motion primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A* algorithm is benchmarked against a very popular heuristic search-based algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in both terms, computation efficiency and motion plan (path) quality.

翻译：在停车场或建筑工地等非结构化环境中，由于搜索空间庞大且车辆受到运动动力学约束，实现实时规划极具挑战性。部分先进规划器采用基于启发式搜索的算法，但这些方法严重依赖用于引导搜索的单一启发函数的质量，因此无法获得合理的计算性能，导致车辆响应出现不必要的延迟。本研究采用多启发式搜索方法，该方法通过利用多个启发函数及其各自优势，可捕获给定搜索空间中的不同复杂性。据我们所知，此前尚未有研究将这一方法应用于该问题。为此，我们定义了多个可采纳与不可采纳的启发函数，扩展了原始多启发式A*搜索以支持双向搜索及混合连续离散搜索空间的处理，并引入了一种运动基元尺度自适应机制。为验证其优势，我们将多启发式A*算法与广受欢迎的基于启发式搜索的混合A*算法进行基准测试比较。实验结果表明，多启发式A*算法在计算效率和运动规划路径质量这两个指标上均优于基线方法。