PDF(Pubmed)
Abstract：
Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.
摘要：
这里,我们提出了自适应合作网络的有效应用，即在大流行或紧急情况下协助残疾人在人群中导航。为了实现这一点，我们对人群运动进行建模，并引入合作学习方法，使健康受损或坐在轮椅上的人群成员能够合作和自我组织，以确保他们在人群中的安全运动。这里,假设每个agent可以估计其他人群成员的移动路径和变化位置。因此,网络节点(代理)应该通过改变它们之间的速度和距离来不断地重组自己，从周围的墙壁，以及在预定义范围内的障碍物。还演示了如何将诸如AirTags之类的可用无线跟踪器用于此目的。针对环境参数的实时变化检查了模型的有效性，并验证了其有效性。
