基于贝叶斯优化的多机器人边界监视可信度模型
Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch
摘要：
在多机器人系统（MRS）越障监视中，确定每一步的监视点以及机器人的位置是否可靠，是保证越障监视有效进行的关键。在本文中，我们开发了一个基于贝叶斯优化的计算可信度模型（CTM）的MRS选择监视点。CTM可以通过参考机器人的态势感知信息，如可通过性和视线，为监视点上的MRS提供实时的可信性评估。该评估可以量化每个机器人在跳跃监视期间保护其机器人团队成员的可信度。可信度评估可以为工作空间中的每个机器人生成动态成本图，并帮助获得最可信的边界监视路径。我们提出的基于贝叶斯的CTM和运动规划可以减少数据汇集中对工作空间的探索次数，提高CTM学习效率。它也使MRS处理动态和不确定的环境中的多机器人跳跃监视任务。在ROS Gazebo中实现了一个机器人仿真，以证明所提出的框架的有效性。
英文摘要：
In multi-robot system (MRS) bounding overwatch, it is crucial to determine which point to choose for overwatch at each step and whether the robots' positions are trustworthy so that the overwatch can be performed effectively. In this paper, we develop a Bayesian optimization based computational trustworthiness model (CTM) for the MRS to select overwatch points. The CTM can provide real-time trustworthiness evaluation for the MRS on the overwatch points by referring to the robots' situational awareness information, such as traversability and line of sight. The evaluation can quantify each robot's trustworthiness in protecting its robot team members during the bounding overwatch. The trustworthiness evaluation can generate a dynamic cost map for each robot in the workspace and help obtain the most trustworthy bounding overwatch path. Our proposed Bayesian based CTM and motion planning can reduce the number of explorations for the workspace in data collection and improve the CTM learning efficiency. It also enables the MRS to deal with the dynamic and uncertain environments for the multi-robot bounding overwatch task. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
