标题： 优化灾难响应中的遍历搜索起始位置 显示英文标题

标题： Optimizing Start Locations in Ergodic Search for Disaster Response

摘要： 在灾难响应场景中，有效部署机器人团队对于提高态势感知和增强搜索和救援行动至关重要。 机器人在搜索和救援中的使用已经过研究，但关于在哪里开始机器人部署的问题尚未得到解决。 本工作通过制定一个联合优化问题，解决了具有异构能力的机器人最优选择起始位置的问题。 为了确定起始位置，本工作向遍历优化框架添加了一个约束，其最小值将机器人分配到起始位置。 当机器人是异构的（配备不同的传感和运动模态）时，这变得稍微更具挑战性，因为并非所有机器人都在同一位置开始，并且应用了上述约束的更复杂的适应方法。 我们的方法假设可以访问潜在的起始位置，这些位置可以从专家知识或航拍图像中获得。 我们通过与使用固定起始位置的基线方法进行比较，对联合优化方法的有效性进行了实验评估。 我们的实验结果表明，在覆盖性能方面有显著提升，就遍历度指标而言，合成数据的平均改进率为35.98%，真实数据的平均改进率为31.91%，适用于同构和异构团队。 显示英文摘要

摘要： In disaster response scenarios, deploying robotic teams effectively is crucial for improving situational awareness and enhancing search and rescue operations. The use of robots in search and rescue has been studied but the question of where to start robot deployments has not been addressed. This work addresses the problem of optimally selecting starting locations for robots with heterogeneous capabilities by formulating a joint optimization problem. To determine start locations, this work adds a constraint to the ergodic optimization framework whose minimum assigns robots to start locations. This becomes a little more challenging when the robots are heterogeneous (equipped with different sensing and motion modalities) because not all robots start at the same location, and a more complex adaptation of the aforementioned constraint is applied. Our method assumes access to potential starting locations, which can be obtained from expert knowledge or aerial imagery. We experimentally evaluate the efficacy of our joint optimization approach by comparing it to baseline methods that use fixed starting locations for all robots. Our experimental results show significant gains in coverage performance, with average improvements of 35.98% on synthetic data and 31.91% on real-world data for homogeneous and heterogeneous teams, in terms of the ergodic metric.