Dynamic Swarm Spatial Scaling for Mobile Sensing Cluster in a Noisy Environment

Abstract

Autonomous mobile devices, such as robots and unmanned aerial vehicles, as alternatives to humans, are expected to be applied to searching for and manipulating a variety of emergent events of which the location and number of occurrences are unknown. When an autonomous mobile device searches for an event, it needs to sense a physical signal emitted by an event, such as radio waves, smell or temperature. After a device finds an event, it must manipulate the event. We previously proposed Mobile Sensing Cluster (MSC), which applies swarm intelligence to multiple autonomous mobile devices to quickly search for and manipulate multiple events using dynamically formed multiple swarms of mobile devices. However, in an environment that the physical signal emitted by an event and sensed by a device includes some random noises, the behavior of swarms in MSC becomes unstable. As a result, MSC requires a long time to search and manipulate. In this paper, we propose a dynamic swarm spatial scaling MSC for improving the tolerance of MSC against such random noises, and show its effectiveness.