AI 中文总结
StochSIPP是针对带不确定边/顶点状态的时间路网的精确条件规划器，通过SIPP与有界AND/OR搜索实现安全导航，可缩短到达时间并解决保守规划器无法处理的门控场景。
AI 中文摘要
在不确定的时变阻塞条件下进行安全导航，需要在执行动作前预判观测结果。本文提出StochSIPP，这是一种针对时间路网的精确条件规划器，其边和顶点的状态在执行过程中会被局部揭示。StochSIPP利用SIPP生成经认证的安全宏动作，这些宏动作会在下次观测或到达目标时终止；并对缓存的动作-观测图进行有界AND/OR搜索，以针对每个可达的观测结果选择动作。乐观型和鲁棒型SIPP松弛方法为有界AND/OR搜索提供可容许的上下界。当确定性声明为安全的每个区间确实安全、感知精确且执行符合规划时序时，所得策略可保证无碰撞。若具备正确的独立概率以及完整的动作和结果生成，该方法可最小化路网和规划时域内的预期到达时间。在受控路网实例上的实验表明，StochSIPP在保持安全固定路径基线观测到的成功率的同时缩短了到达时间，且能解决保守固定路径规划器无法生成方案的门控场景；可扩展性研究进一步显示，随着同时观测到的不确定状态数量增加，其性能快速提升。
英文摘要
Safe navigation under uncertain time-dependent blockage requires anticipating observations before committing to motion. We present StochSIPP, an exact contingent planner for temporal roadmaps with uncertain edge and vertex statuses revealed locally during execution. StochSIPP uses SIPP to generate certified-safe macro-actions that terminate at the next observation or the goal, and bounded AND/OR search over a cached action--observation graph to select actions for every reachable observation outcome. Optimistic and robust SIPP relaxations provide admissible lower and upper bounds for bounded AND/OR search. When every interval declared deterministically safe is truly safe, sensing is exact, and execution follows the planned timing, the resulting policy is provably collision-free. With correct independent probabilities and complete action and outcome generation, it minimizes expected arrival time within the roadmap and horizon. Experiments on controlled roadmap instances show that StochSIPP preserves the observed success of safe fixed-path baselines while reducing arrival time, and solves gated scenarios in which conservative fixed-path planners return no plan. A scalability study further reveals rapid growth as the number of simultaneously observed uncertain statuses increases.
Comments16 pages, 4 figures
