提出风险感知的规划与评估方法，提升水下机器人长期自主任务可靠性。
Risk-Averse Planning and Plan Assessment for Marine Robots
- 先生成多样高层计划，再在低层仿真中评估选最优最可靠方案
- 在真实水下仿真中验证，不同场景下均能有效降低执行风险
- 适合需要长期无人干预的海洋探测、勘探等任务
自主水下航行器（AUV）需在无人员干预下连续工作数日，因此必须具备高效可靠的任务规划能力。然而，为保证可扩展性而进行的领域模型抽象，常导致实际执行中计划不可靠或表现不佳。最优的抽象计划在物理执行中可能变为次优或不可靠。为此，本文提出一种方法：首先生成一组多样化的高层计划，再通过低层仿真评估其性能与风险，最终选择最优且最可靠的候选方案。我们在一个真实的水下机器人仿真环境中评估该方法，对多种场景下的风险指标进行估算，验证了该方法的可行性与有效性。
原文摘要 · Abstract (English)
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.
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