Self-Cloning Mobile Sensing Cluster based on Swarm Intelligence with Multiple Autonomous Mobile Systems
Shoma Nishigami, Eiji Nii, Naoki Fujiyama, Shoma Izuhara, Hiroyuki Yomo, Yasuhisa Takizawa · 2023
In real environments, it is difficult to predict the occurrence of events such as damage to structures and people in need of rescue, and the location and number of most such events are unknown. We have proposed a Mobile Sensing Cluster (MSC) to search for and capture unknown events. MSC dynamically forms multiple swarms of autonomous mobile systems such as robots and UAVs based on swarm intelligence, and it achieves search and capture of more unknown events in a short time. However, the effectiveness of MSC strongly depends on the number of autonomous mobile systems used. In this paper, we propose a Self-Cloning Mobile Sensing Cluster to achieve the search and capture of unknown events beyond the actual number of autonomous mobile systems, and we discuss its basic effectiveness based on an evaluation using simulations. The Self-Cloning MSC consists of both actual mobile systems in physical space and virtual cloned mobile entities in cyber space. This virtually increases the number of autonomous mobile systems with the cloned mobile entities, allowing them to form diverse swarms based on MSC.