Multi-agent exploration of spatial dynamical processes under sparsity constraints

Kurzfassung

This paper addresses the development of an efficient information gathering and exploration strategy for robotic missions when a high level of autonomy is expected. A multiagent system is considered, which consists of several mobile sensing platforms with the goal to identify the parameters of a spatio-temporal process modeled by a partial differential equation. Specifically, an exploration of a diffusion process driven by an unknown number of sparsely located sources is considered. A probabilistic approach toward partial differential equations and sparsity constraints modeling with factor graphs is developed and realized by a customized message passing algorithm. The algorithm permits efficient identification of source parameters: the number of sources, their locations and amplitudes. In addition, an exploration strategy to guide the agents to more informative sampling locations is proposed; this accelerates identification of the source parameters. The message passing implementation facilitates efficient distributed implementation, which is of significant advantage with respect to scalability, computational complexity and an implementation in a multi-agent system. The effectiveness of the algorithm is demonstrated using synthetic data in simulations.