Cooperative n-boundary tracking in large scale environments

Abstract

Monitoring in large scale environments is a typical mission in cooperative robotics. This task requires the exploration of a huge domain by a generally small number of sensor equipped mobile robots. As time restrictions prohibit an exhaustive global search, a sampling strategy is required that allows an efficient spatial mapping of the environment. This paper proposes an adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs). The approach combines uncertainty and correlation-based concentration estimates to generate sampling points based on already gathered data. The adaptive generation of sampling locations is coupled to a distributed modelpredictive controller for planning optimal vehicle trajectories under collision and communication constraints. Simulation results demonstrate that connectivity of all involved vehicles can be maintained and an accurate reconstruction of the plume is obtained efficiently.