Tree-In-Motion Mapping: A Multi-Agent Solution for Environment Monitoring

The active investigation of complex environmental parameters involves many computationally-intensive operations, such as data collection, aggregation, dissemination and processing. The adoption of multi-agent systems appears as a very promising approach in the development of a new generation of intelligent autonomous robotic agents for complex environment monitoring. This paper discusses a multi-agent system whose global goal is the minimization of entropy of an environment, based on a novel tree in-motion mapping method. The proposed method combines the simplicity and speed of computation, along with low storage and communications requirements, which ideally fits a system composed of a finite number of mobile robotic agents, each possessing limited sensing, processing and communicating operational entities. The method is also extensible to include multiple factors into the determination of the environmental entropy, hence allowing measurands such as electromagnetic, thermal and optical energies to be aggregated in the cost function. Simulation results demonstrate the efficiency of the method, compare the centralized and decentralized approaches, and present the savings, both in processing and communication times, when compared to existing methods.