Information-Based Control of Robots in Search-and-Rescue Missions With Human Prior Knowledge
Information-Based Control of Robots in Search-and-Rescue Missions With Human Prior Knowledge
复制标题
基于人类先验知识的搜救机器人信息控制
DOI：
10.1109/thms.2021.3113642
复制
发表时间：
2021-11-16
影响因子：
3.6
通讯作者：
Butail, Sachit
中科院分区：
文献类型：
--
作者：
Krzysiak, Rafal;Butail, Sachit
多机器人系统为监控任务提供了可扩展且强大的解决方案。在搜索和救援等时间密集型任务中，人类的参与具有结合有关目标位置或动态的先验知识的潜在优势。在本文中，我们开发了一个通用信息理论框架，用于在搜索和救援任务中控制多个自主机器人，其中包括人类远程操作员。对人类先验知识进行建模以捕获目标位置和动态，并制定基于互信息的控制以使自主机器人在两种策略之间进行权衡：独立搜索或通过保持接近来协助人类。控制动作优化使用目标和参考机器人的粒子滤波估计计算出的归一化互信息的加权和。我们实现该框架来模拟在文献中的搜索和救援任务之后设计的两种截然不同的场景，并将不同程度的准确性纳入人类先验知识中。我们的结果表明，任务性能取决于机器人在两种策略之间的权衡，以及受先验知识和机器人数量影响的策略之间共享的最优控制工作量。与现有策略的比较表明，在人类先验知识不准确的情况下，基于信息的控制具有优势。所提出的人机交互的信息理论抽象可以在各种场景中实现，结果强调了人类先验知识在时间密集型任务中对有效机器人协助的作用。
Multirobot systems provide a scalable and robust solution for monitoring tasks. In time-intensive missions such as search and rescue, the inclusion of a human has the potential advantage of incorporating prior knowledge about the target location or dynamics. In this article, we develop a general information-theoretic framework to control multiple autonomous robots in search and rescue missions that include a human teleoperator. Human prior knowledge is modeled to capture the target location and dynamics, and mutual-information-based control is formulated to let autonomous robots weigh between two strategies: independent search or assisting the human by staying in proximity. The control actions optimize a weighted sum of normalized mutual information calculated using particle-filtered estimates of the target and the reference robot. We implement the framework to simulate two widely different scenarios designed after search-and-rescue missions from literature, and incorporate varying levels of accuracy in human prior knowledge. Our results indicate that the mission performance depends on how robots weigh between the two strategies, with the amount of the optimal control effort shared between strategies affected by prior knowledge and the number of robots. Comparison with existing strategies points to the benefits of an information-based control in situations where human prior knowledge is inaccurate. The proposed information-theoretic abstraction of the human-robot interaction can be implemented on a wide variety of scenarios and the results highlight the role of human prior knowledge toward effective robotic assistance in time-intensive missions.