Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions -- a method for efficient distributed sensor planning -- applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.
翻译:勘探和感知任务的数量目标力求在一台或多台摄影机观察在未知环境中运行的机器人的假设观测中找到一种价值感(或奖赏)。例如,数量目标可能根据所要观测的未知空间的预期数量按比例奖励机器人。我们从预期的覆盖范围方面确定现有信息理论和覆盖范围目标之间的联系,特别是无噪音的相互信息是预期覆盖范围的一个特殊案例。同样,我们首次比较了基于信息的近似值和勘探的覆盖范围目标,我们发现,也许令人惊讶的是,覆盖目标实际上大大超过基于信息的目标。此外,对信息和覆盖范围目标的分析表明,随机序列分布式传感器规划方法适用于两类目标,我们为多达32个机器人提供各种环境中的模拟结果。