We present a new algorithm for deploying passenger robots in marsupial robot systems. A marsupial robot system consists of a carrier robot (e.g., a ground vehicle), which is highly capable and has a long mission duration, and at least one passenger robot (e.g., a short-duration aerial vehicle) transported by the carrier. We optimize the performance of passenger robot deployment by proposing an algorithm that reasons over uncertainty by exploiting information about the prior probability distribution of features of interest in the environment. Our algorithm is formulated as a solution to a sequential stochastic assignment problem (SSAP). The key feature of the algorithm is a recurrence relationship that defines a set of observation thresholds that are used to decide when to deploy passenger robots. Our algorithm computes the optimal policy in $O(NR)$ time, where $N$ is the number of deployment decision points and $R$ is the number of passenger robots to be deployed. We conducted drone deployment exploration experiments on real-world data from the DARPA Subterranean challenge to test the SSAP algorithm. Our results show that our deployment algorithm outperforms other competing algorithms, such as the classic secretary approach and baseline partitioning methods, and is comparable to an offline oracle algorithm.
翻译:我们为在无人机机器人系统中部署客运机器人提供了一种新的算法。 无人机机器人系统由载体机器人(如地面车辆)组成,该机器人高度能力强,任务期限长,而且至少由承运人运输一个客运机器人(如短期航空飞行器)组成。 我们通过提出一种算法优化了客运机器人部署的绩效,该算法通过利用有关环境利益特征先前概率分布的信息来说明不确定的原因。 我们的算法是作为连续随机分配问题(SASAP)的一种解决办法制定的。 算法的关键特征是确定一套用于决定何时部署客运机器人的观察阈值的复现关系。 我们的算法以美元(NR)时间计算最佳政策,其中美元是部署决定点的数目,而美元是将要部署的客运机器人的数目。 我们对DARPA Subterrane对测试SAP算法提出的现实世界数据进行了无人机部署实验。 我们的计算结果显示,我们的部署算法超越了用来决定何时部署客运机器人的一套观察阈值。 我们的算法是其他可比较的算法,例如典型的秘书。