论文标题
D2A-BSP：蒸馏数据关联信念空间规划及其在预算限制下的性能保证
D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints
论文作者
论文摘要
模棱两可和感知混乱的环境中未解决的数据关联导致机器人和环境状态都有多模式的假设。为了避免灾难性的结果，在这种模棱两可的环境中运行时，对于信念空间计划（BSP）中的数据关联的推理至关重要。但是，明确考虑所有可能的数据关联，假设的数量随规划范围而呈指数增长，并确定最佳动作序列很快就变得棘手了。此外，在必须修剪一些不可忽略的假设的艰难预算限制下，实现绩效保证是至关重要的。在这项工作中，我们提出了一种计算有效的新方法，该方法仅利用一个蒸馏的假设来解决BSP问题，同时推理了数据关联。此外，为了提供性能保证，我们在最佳解决方案方面得出了误差界限。然后，我们在极为混乱的环境中演示了我们的方法，在那里我们设法大大缩短了计算时间，而不会损害解决方案的质量。
Unresolved data association in ambiguous and perceptually aliased environments leads to multi-modal hypotheses on both the robot's and the environment state. To avoid catastrophic results, when operating in such ambiguous environments, it is crucial to reason about data association within Belief Space Planning (BSP). However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon and determining the optimal action sequence quickly becomes intractable. Moreover, with hard budget constraints where some non-negligible hypotheses must be pruned, achieving performance guarantees is crucial. In this work we present a computationally efficient novel approach that utilizes only a distilled subset of hypotheses to solve BSP problems while reasoning about data association. Furthermore, to provide performance guarantees, we derive error bounds with respect to the optimal solution. We then demonstrate our approach in an extremely aliased environment, where we manage to significantly reduce computation time without compromising on the quality of the solution.
