论文标题
具有多模式感应的自适应信息路径计划
Adaptive Informative Path Planning with Multimodal Sensing
论文作者
论文摘要
自适应信息路径计划（AIPP）问题模型的代理商要在未知的，部分可观察到的环境中获得资源约束的代理商。 AIPP上的现有工作重点是代表因代理人运动而对世界的观察。我们制定了更通用的环境，除了穿越环境以收集信息之外，代理商可以以某些能量为代价在不同的传感器之间进行选择。我们称此问题AIPPMS（用于多模式传感的MS）。 AIPPMS需要根据消耗和获得的能量来共同推理感应和运动的影响。我们将AIPPMS构架为可观察到的马尔可夫决策过程（POMDP），并通过在线计划解决。我们的方法基于部分可观察到的蒙特卡洛计划框架，并进行了修改，以确保可行性和针对AIPPMS量身定制的启发式推出政策。我们在两个领域上评估了我们的方法：模拟的搜索场景和经典摇滚问题问题的挑战性扩展。我们发现，我们的方法的表现优于经典的AIPP算法，该算法已修改为AIPPMS，以及使用随机推出策略的在线计划。
Adaptive Informative Path Planning (AIPP) problems model an agent tasked with obtaining information subject to resource constraints in unknown, partially observable environments. Existing work on AIPP has focused on representing observations about the world as a result of agent movement. We formulate the more general setting where the agent may choose between different sensors at the cost of some energy, in addition to traversing the environment to gather information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained. We frame AIPPMS as a Partially Observable Markov Decision Process (POMDP) and solve it with online planning. Our approach is based on the Partially Observable Monte Carlo Planning framework with modifications to ensure constraint feasibility and a heuristic rollout policy tailored for AIPPMS. We evaluate our method on two domains: a simulated search-and-rescue scenario and a challenging extension to the classic RockSample problem. We find that our approach outperforms a classic AIPP algorithm that is modified for AIPPMS, as well as online planning using a random rollout policy.
