  Autonomous aerial robots provide new possibilities to study the habitats and
behaviors of endangered species through the efficient gathering of location
information at temporal and spatial granularities not possible with traditional
manual survey methods. We present a novel autonomous aerial vehicle
system-TrackerBots-to track and localize multiple radio-tagged animals. The
simplicity of measuring the received signal strength indicator (RSSI) values of
very high frequency (VHF) radio-collars commonly used in the field is exploited
to realize a low cost and lightweight tracking platform suitable for
integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the
nonlinearity of the system based on RSSI measurements, our tracking and
planning approaches integrate a particle filter for tracking and localizing; a
partially observable Markov decision process (POMDP) for dynamic path planning.
This approach allows autonomous navigation of a UAV in a direction of maximum
information gain to locate multiple mobile animals and reduce exploration time;
and, consequently, conserve onboard battery power. We also employ the concept
of a search termination criteria to maximize the number of located animals
within power constraints of the aerial system. We validated our real-time and
online approach through both extensive simulations and field experiments with
two mobile VHF radio-tags.
