A potential field approach to finding minimum-exposure paths in wireless sensor networks

A novel artificial-potential approach is presented for planning the minimum-exposure paths of multiple vehicles in a dynamic environment containing multiple mobile sensors, and multiple fixed obstacles. This approach presents several advantages over existing techniques, such as the ability of computing multiple minimum-exposure paths online, while avoiding mutual collisions, as well as collisions with obstacles sensed during the motion. Other important advantages include the ability of utilizing heterogenous sensor models, and of meeting multiple objectives, such as minimizing power required, and reaching a set of goal configurations. The approach is demonstrated through numerical simulations involving autonomous underwater vehicles (AUVs) deployed in a region of interest near the New Jersey coast, with ocean currents simulated using real coastal ocean dynamics applications radar (CODAR) data.