A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale

Background In this article, we model a behavior-based strategy of autonomous coverage and exploration at the scale of a city with multiple robots. The behavioral components are motivated by Cepeda et al. (Sensors 12 (9): 12772–12797, 2012) and extended to incorporate into a generic cellular-automata based agent model. These agents are representing homogenous robots with reactive control. Deliberative approaches requires large scale map and large memory, which slowdowns the execution. Our approach is reactive and simple, that is, robots have no prior information about the environment and do not generate a route map as they traverse. However, other robots in neighborhood are detected using local sensors. Findings A city-scale map-driven simulation is designed and model’s efficiency is evaluated for different deployment possibilities. It is evidenced that even with this simple model, the agents are able to explore a significant percentage of the environment. Conclusion For a city-scale multi-robotic exploration, our simple but efficient model does not require explicit communication and data sharing (and hence representation and storage of navigated map) because possibility of encountering and influencing another agent is quite low, due to spatial dynamics of the environment.