Sensing-System Reconfiguration: A Comparison of On-Line Methods

This paper investigates the performance of two dispatching approaches applied to the real-time coordination of multiple, mobile sensors. The sensing system is targeted towards the surveillance of objects in the context of autonomous manufacturing systems. Sensors are assigned and manoeuvred to collect data at specific points on the object trajectory. A technique based on reinforcement learning (RL) is compared to a heuristic dispatching method and a system that does not use dispatching at all. Through a number of simulation examples, it is shown that, on average, the RL-based dispatcher achieves very similar, if not slightly better, performance than the heuristic dispatcher. Both approaches appear to provide a benefit over non-dispatching systems, thereby validating the efficacy of the dispatching approach, despite very different underlying implementations.