Autonomous Underwater Vehicles (AUVs) need to operate for days without human intervention and thus must be able to do efficient and reliable task planning. Unfortunately, efficient task planning requires deliberately abstract domain models (for scalability reasons), which in practice leads to plans that might be unreliable or under performing in practice. An optimal abstract plan may turn out suboptimal or unreliable during physical execution. To overcome this, we introduce a method that first generates a selection of diverse high-level plans and then assesses them in a low-level simulation to select the optimal and most reliable candidate. We evaluate the method using a realistic underwater robot simulation, estimating the risk metrics for different scenarios, demonstrating feasibility and effectiveness of the approach.

翻译：自主水下航行器（AUVs）需在无人干预条件下持续运行数日，因此必须具备高效可靠的任务规划能力。然而，高效任务规划通常需要构建经过刻意简化的领域模型（出于可扩展性考量），这在实际应用中可能导致生成的计划可靠性不足或执行效果欠佳。理论上最优的抽象计划在物理执行过程中可能表现出次优或不稳定的特性。为解决该问题，本文提出一种方法：首先生成多样化高层计划集合，随后通过底层仿真评估这些计划，从而筛选出最优且最可靠的候选方案。我们采用真实水下机器人仿真环境对该方法进行评估，通过不同场景下的风险指标测算，验证了该方法的可行性与有效性。