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.

翻译：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方向。这一新兴增长趋势增加了对能够兼顾人类偏好多智能体算法的设计需求。本文提出了一种蚁群优化（ACO）元启发式算法的扩展，以解决在人类与机器人共同执行目标搜索任务情况下的最短时间搜索（MTS）问题。所提出的模型包含两个主要模块。第一个是卷积神经网络（CNN），它通过分割图像提供目标可能位置的先验概率。第二个是子先验MTS-ACO算法（SP-MTS-ACO），该算法以先验概率以及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通过平板电脑上的Vizanti基于网络的可视化系统，针对联合搜索目标的任务进行了测试。所设计的接口实现了人类与我们名为IVO的人形机器人之间的通信。获得的结果表明，在未损失效率的前提下，用户的搜索感知能力得到了提升。