Structured Isosurface Mapping of 3D Scalar Fields with Mobile Sensor Networks

Adaptive Navigation (AN) strategies compute and adjust a vehicle's trajectory using realtime measurements of the environment. AN techniques can reduce time and energy needed to explore scalar characteristics (e.g., temperature, humidity) of regions of interest when compared to conventional navigation methods. Mobile sensor networks can improve AN performance by allowing for cooperative and spatially-dispersed sensing to attain realtime information of the local scalar field structure, which can be used to inform navigation decisions. This paper presents results of a continuing program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using our unique multilayer control architecture for multiple vehicles. In particular, this work presents a new controller for isosurface mapping with structured slicing that is perpendicular to the estimated local field gradient. This can result in more cost-effective and efficient surface characterization while also simplifying operation for a supervisor. We verify functionality using high-fidelity simulations of drone clusters which account for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise.