论文标题
在预算限制下
Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints
论文作者
论文摘要
理想情况下，在感知混乱的环境中运行的自主代理应该能够解决数据关联问题。但是，在考虑此问题的同时计划将来的行动并不是一件容易的事。因此，艺术的方法使用多模式假设来代表代理和环境的状态。但是，明确考虑所有可能的数据关联，假设的数量随着计划范围而成倍增长。因此，相应的信念空间计划问题很快变得无法解决。此外，在严格的计算预算限制下，一些不可忽视的假设最终必须在计划和推论中修剪。然而，这两个过程通常是单独处理的，并且几乎没有研究一个过程中预算限制的影响。我们提出了一种计算有效的方法，可以在有关数据关联的推理时解决非主张信念空间计划问题。此外，我们严格分析预算限制在推理和计划中的影响。
Autonomous agents operating in perceptually aliased environments should ideally be able to solve the data association problem. Yet, planning for future actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon. As such, the corresponding Belief Space Planning problem quickly becomes unsolvable. Moreover, under hard computational budget constraints, some non-negligible hypotheses must eventually be pruned in both planning and inference. Nevertheless, the two processes are generally treated separately and the effect of budget constraints in one process over the other was barely studied. We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association. Moreover, we rigorously analyze the effects of budget constraints in both inference and planning.
