基于蚁群优化中子先验的人机协作最小时间搜索
Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization
AI总结：
本文提出SP-MTS-ACO算法，结合CNN先验概率与智能体子先验偏好，解决人机协作最小时间搜索问题，并在真实实验中验证了其在保持效率的同时提升用户搜索感知。
摘要：
人机协作（HRC）由于人工智能（AI）和人机交互（HRI）等领域的最新突破，已发展成为一个极具前景的研究问题。这一新兴增长增加了设计能够同时管理人类偏好的多智能体算法的需求。本文提出了蚁群优化（ACO）元启发式算法的一种扩展，用于解决最小时间搜索（MTS）任务，在该任务中人类和机器人共同执行物体搜索任务。所提出的模型由两个主要模块组成。第一个模块是一个卷积神经网络（CNN），它根据分割后的图像提供物体可能所在位置的先验概率。第二个模块是子先验MTS-ACO算法（SP-MTS-ACO），该算法以先验概率和智能体在不同子先验中的特定搜索偏好作为输入，为所有智能体生成搜索计划。该模型已在真实实验中进行了测试，通过平板电脑上基于Vizanti的网络可视化界面实现人与机器人对物体的联合搜索。所设计的界面允许人类与名为IVO的仿人机器人之间进行通信。获得的结果表明，在不损失效率的情况下，用户的搜索感知得到了改善。
英文摘要：
Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among other reasons. This emerging growth increases the need to design multi-agent algorithms that can manage also human preferences. This paper presents an extension of the Ant Colony Optimization (ACO) meta-heuristic to solve the Minimum Time Search (MTS) task, in the case where humans and robots perform an object searching task together. The proposed model consists of two main blocks. The first one is a convolutional neural network (CNN) that provides the prior probabilities about where an object may be from a segmented image. The second one is the Sub-prior MTS-ACO algorithm (SP-MTS-ACO), which takes as inputs the prior probabilities and the particular search preferences of the agents in different sub-priors to generate search plans for all agents. The model has been tested in real experiments for the joint search of an object through a Vizanti web-based visualization in a tablet computer. The designed interface allows the communication between a human and our humanoid robot named IVO. The obtained results show an improvement in the search perception of the users without loss of efficiency.
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。
