Cooperative navigation for heterogeneous autonomous vehicles via approximate dynamic programming

Unmanned ground and aerial vehicles are becoming crucial to many applications because of their ability to assist humans in carrying out dangerous missions. These vehicles can be viewed as networks of heterogeneous unmanned robotic sensors with the goal of exploring complex environments, to search for and, possibly, pursue moving targets. The robotic vehicle performance can be greatly enhanced by implementing future sensor actions intelligently, based both on prior knowledge and on the information obtained by the sensors on line. In this paper, we present an approximate dynamic programming (ADP) approach to cooperative navigation for heterogeneous sensor networks. The mobile sensor network consists of a set of robotic sensors modeled as hybrid systems with processing capabilities. The goal of the ADP algorithm is to coordinate a team of heterogeneous autonomous vehicles (i.e., ground robot and quadrotor UAV) to navigate within an obstacle populated environment while satisfying collision avoidance constraints and searching for stationary and mobile targets. It is assumed that the ground vehicle has a small sensor footprint with high resolution. The quadrotor, on the other hand, has a large sensor field-of-view but low resolution. The UAV provides a low resolution look-ahead map to the ground robot which in turn uses this information to plan its actions. The proposed navigation strategy combines artificial potential functions for target pursuing with ADP for learning C-obstacles on line. The efficacy of the proposed methodology is verified through numerical simulations.