Prioritized Robotic Exploration with Deadlines: A Comparison of Greedy, Orienteering, and Profitable Tour Approaches
Prioritized Robotic Exploration with Deadlines: A Comparison of Greedy, Orienteering, and Profitable Tour Approaches
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DOI：
10.1109/icra48891.2023.10161118
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
S. Datta;Srinivas Akella
中科院分区：
文献类型：
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作者：
S. Datta;Srinivas Akella
本文解决了机器人探索未知室内环境的最后期限问题。使用移动机器人的室内探索通常侧重于探索整个环境，而不考虑截止日期。本文的优先探索目标是通过探索环境的关键区域并在最后期限内返回到初始位置，快速计算出初始未知环境的几何布局。这种优先级勘探对于时间紧迫和危险的环境非常有用，在这些环境中，机器人的快速勘探可以为后续操作提供重要信息。例如，对于时间至关重要的消防员来说，他们可以利用机器人探索生成的地图来导航着火的建筑物。在我们之前的工作中，我们证明了基于优先级的贪婪算法在最后期限下的探索可以优于基于成本的贪婪算法。本文将优先勘探问题建模为定向问题（Orienteering problem， OP）和盈利旅游问题（profitability Tour problem， PTP），试图生成能够在给定时间内探索更大比例环境的勘探策略。本文给出了基于多图形和Gazebo环境下的仿真结果。我们发现，在许多情况下，基于优先级的贪婪算法的性能与基于OP和基于ptp的算法相当或更好。我们分析了造成这一反直觉结果的潜在原因。
This paper addresses the problem of robotic exploration of unknown indoor environments with deadlines. Indoor exploration using mobile robots has typically focused on exploring the entire environment without considering deadlines. The objective of the prioritized exploration in this paper is to rapidly compute the geometric layout of an initially unknown environment by exploring key regions of the environment and returning to the home location within a deadline. This prioritized exploration is useful for time-critical and dangerous environments where rapid robot exploration can provide vital information for subsequent operations. For example, firefighters, for whom time is of the essence, can utilize the map generated by this robotic exploration to navigate a building on fire. In our previous work, we showed that a priority-based greedy algorithm can outperform a cost-based greedy algorithm for exploration under deadlines. This paper models the prioritized exploration problem as an Orienteering Problem (OP) and a Profitable Tour Problem (PTP) in an attempt to generate exploration strategies that can explore a greater percentage of the environment in a given amount of time. The paper presents simulation results on multiple graph-based and Gazebo environments. We found that in many cases the priority-based greedy algorithm performs on par or better than the OP and PTP-based algorithms. We analyze the potential reasons for this counterintuitive result.