Optimal Sequential Stochastic Deployment of Multiple Passenger Robots
Optimal Sequential Stochastic Deployment of Multiple Passenger Robots
复制标题
多个载人机器人的最优顺序随机部署
DOI：
10.1109/icra48506.2021.9561059
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Geoffrey A. Hollinger
中科院分区：
文献类型：
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作者：
C. Lee;Graeme Best;Geoffrey A. Hollinger
我们提出了一种在有袋动物机器人系统中部署乘客机器人的新算法。有袋类机器人系统由能力强、任务持续时间长的运载机器人（例如地面车辆）和至少一个由运载器运输的载客机器人（例如短时飞行器）组成。我们通过提出一种算法来优化乘客机器人部署的性能，该算法通过利用环境中感兴趣的特征的先验概率分布信息来推理不确定性。我们的算法被制定为顺序随机分配问题（SSAP）的解决方案。该算法的关键特征是递归关系，它定义了一组观察阈值，用于决定何时部署乘客机器人。我们的算法在 O(NR) 时间内计算最优策略，其中 N 是部署决策点的数量，R 是要部署的乘客机器人的数量。我们利用 DARPA 地下挑战赛的真实数据进行了无人机部署探索实验，以测试 SSAP 算法。我们的结果表明，我们的部署算法优于其他竞争算法，例如经典的秘书方法和基线分区方法，并且与离线预言机算法相当。
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.