Robots have the potential to perform search for a variety of applications under different scenarios. Our work is motivated by humanitarian assistant and disaster relief (HADR) where often it is critical to find signs of life in the presence of conflicting criteria, objectives, and information. We believe ergodic search can provide a framework for exploiting available information as well as exploring for new information for applications such as HADR, especially when time is of the essence. Ergodic search algorithms plan trajectories such that the time spent in a region is proportional to the amount of information in that region, and is able to naturally balance exploitation (myopically searching high-information areas) and exploration (visiting all locations in the search space for new information). Existing ergodic search algorithms, as well as other information-based approaches, typically consider search using only a single information map. However, in many scenarios, the use of multiple information maps that encode different types of relevant information is common. Ergodic search methods currently do not possess the ability for simultaneous nor do they have a way to balance which information gets priority. This leads us to formulate a Multi-Objective Ergodic Search (MOES) problem, which aims at finding the so-called Pareto-optimal solutions, for the purpose of providing human decision makers various solutions that trade off between conflicting criteria. To efficiently solve MOES, we develop a framework called Sequential Local Ergodic Search (SLES) that converts a MOES problem into a "weight space coverage" problem. It leverages the recent advances in ergodic search methods as well as the idea of local optimization to efficiently approximate the Pareto-optimal front. Our numerical results show that SLES runs distinctly faster than the baseline methods.
翻译:机器人具有在不同情景下搜索各种应用的潜力。 我们的工作是由人道主义助理和救灾(HADR)推动的, 在那里,在标准、目标和信息相互冲突的情况下寻找生命迹象往往至关重要。 我们认为, 自动搜索可以提供一个框架, 用于利用现有信息, 以及探索新的信息, 例如 自动搜索, 特别是当时间是关键的时候。 Ergodic 搜索算法计划轨迹, 使得在一个区域花费的时间与该地区的信息量成比例, 并且能够自然平衡利用( 近距离搜索高信息区) 和探索( 访问搜索空间中所有地点的新信息) 。 我们认为, 现有的自动搜索算法和其他基于信息的方法, 通常会考虑只使用单一信息图搜索。 但是, 在许多情景中, 使用多种信息地图来编码不同类型相关信息。 刻度搜索方法目前不具有同步能力, 也不可能平衡信息成为优先事项。 这导致我们设计多层次搜索SOVOOgoogo S 的视野, 目标就是在人类交易中找到一个“ 快速搜索” 的“ 数字” 目标,, 也就是搜索工具,, 搜索, 也就是搜索是用来在人类交易中找到一个“ 我们的“ 的“ 数字” 的“ 数字” 的“ 定义” 的“ 的“ 的“ ” 的“ ” 的”, 定义” 定义” 的“快速”,, 的“快速” 的“快速”,, 的“我们的“快速” 的” 的“快速”,,, 的“快速”,, 的“快速” 的” 的“快速” 定义的“ 的“我们的“我们的“ 的“ 的” 的” ” 的” 的” 的” 的” ” ” 的” 的“快速”, 的“快速”, 的“快速”, 的“快速” 的”, 的“,,,,,, 的“ 的“ 的” 的” 的”,,, 的“ ” ”,,,,, 的“ 的“