Object surveillance using reinforcement learning based sensor dispatching

This paper outlines an approach to the coordination of multiple mobile sensors for the surveillance of a single moving target. A real-time dispatching algorithm is used to select and position groups of sensors in response to the observed object motion. The aim is to provide robust, high-quality data while ensuring that the system can react to unexpected object manoeuvres. Sensors are assigned to collect data at specific points on the object trajectory. A dispatching strategy learned via reinforcement learning is used to control the sensor poses with respect to these points. In using the learned strategy, each sensor adopts an egocentric view of the system state to determine the most appropriate action. Simulations demonstrate the performance of the RL-based dispatcher, in comparison to similar static-sensor systems.