人机协作搜索中，用深度学习先验+蚁群算法提升寻物效率。
Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization
- 用CNN提取图像先验概率，指导搜索方向
- 引入子先验机制，兼顾人机偏好差异，搜索时间减少17%
- 真实场景实验验证，用户体验提升且不牺牲效率
人机协作（HRC）因人工智能与人机交互的最新突破而成为重要研究方向，亟需能融合人类偏好的多智能体算法。本文提出一种改进的蚁群优化（ACO）元启发式算法——子先验最小时间搜索蚁群算法（SP-MTS-ACO），用于解决人机协同搜物任务中的最小时间搜索问题。该模型包含两个核心模块：第一是卷积神经网络（CNN），从分割图像中生成物体可能位置的先验概率；第二是SP-MTS-ACO算法，结合各智能体在不同子先验下的搜索偏好，生成联合搜索计划。实验在基于Vizanti的可视化网页平台上进行，通过平板电脑实现人与名为IVO的人形机器人之间的交互。结果表明，用户搜索感知显著改善，且未降低整体搜索效率。
原文摘要 · Abstract (English)
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.
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