Multi-Agent Path Finding (MAPF) algorithms, including those for car-like robots and grid-based scenarios, face significant computational challenges due to expensive heuristic calculations. Traditional heuristic caching assumes that the heuristic function depends only on the state, which is incorrect in constraint-based search algorithms (e.g., CBS, MAPF-LNS, MAP2) where constraints from conflict resolution make the search space context-dependent. We propose \textbf{CAHC} (Conflict-Aware Heuristic Caching), a general framework that caches heuristic values based on both state and relevant constraint context, addressing this fundamental limitation. We demonstrate CAHC through a case study on CL-CBS for car-like robots, where we combine conflict-aware caching with an adaptive hybrid heuristic in \textbf{CAR-CHASE} (Car-Like Robot Conflict-Aware Heuristic Adaptive Search Enhancement). Our key innovations are (1) a compact \emph{conflict fingerprint} that efficiently encodes which constraints affect a state's heuristic, (2) a domain-adaptable relevance filter using spatial, temporal, and geometric criteria, and (3) a modular architecture that enables systematic application to diverse MAPF algorithms. Experimental evaluation on 480 CL-CBS benchmark instances demonstrates a geometric mean speedup of 2.46$\times$ while maintaining solution optimality. The optimizations improve success rate from 77.9\% to 84.8\% (+6.9 percentage points), reduce total runtime by 70.1\%, and enable solving 33 additional instances. The framework's general architecture makes it applicable as a reliable optimization technique for MAP2, MAPF-LNS, and other constraint-based MAPF algorithms.

翻译：多智能体路径规划（MAPF）算法，包括面向类车机器人和基于网格场景的算法，由于启发式计算成本高昂而面临显著的计算挑战。传统的启发式缓存假设启发函数仅依赖于状态，这在基于约束的搜索算法（例如CBS、MAPF-LNS、MAP2）中是不正确的，其中来自冲突消解的约束使得搜索空间具有上下文依赖性。我们提出了\textbf{CAHC}（冲突感知启发式缓存），这是一个基于状态和相关约束上下文缓存启发值的通用框架，解决了这一根本性局限。我们通过对类车机器人的CL-CBS进行案例研究来展示CAHC，其中我们将冲突感知缓存与自适应混合启发式方法结合在\textbf{CAR-CHASE}（类车机器人冲突感知启发式自适应搜索增强）中。我们的关键创新在于：（1）一种紧凑的\emph{冲突指纹}，能高效编码哪些约束影响状态的启发值；（2）一种使用空间、时间和几何准则的可适应领域相关性过滤器；（3）一种模块化架构，支持系统性地应用于多种MAPF算法。在480个CL-CBS基准实例上的实验评估表明，在保持解最优性的同时，获得了2.46$\times$的几何平均加速比。优化将成功率从77.9\%提升至84.8\%（+6.9个百分点），总运行时间减少了70.1\%，并额外解决了33个实例。该框架的通用架构使其可作为MAP2、MAPF-LNS及其他基于约束的MAPF算法的可靠优化技术。