Heterogeneous Sensor-Robot Team Positioning and Mixed Strategy Scheduling

Abstract

We are faced with the problem of optimally placing a heterogeneous team of sensors and effector robots in an area while taking into account the environment, anticipated arrival traffic, and desired power consumption of the team. We stage the problems of anticipating arrival traffic and determining a proper power schedule as an adversarial game, incorporating our analysis of the game in the objective function which evaluates sensor positions. We obtain the set of sensor positions which performs best at the desired power consumption, evaluating the mixed strategy of sensor activity that best counters the anticipated potential arrival paths. To determine an approximate global optima for a large number of heterogeneous nodes, we employ Adaptive Simulated Annealing (ASA) to ensure our algorithm is flexible over a varied range of scenarios. We compare the proposed algorithm to a gradient-based greedy placement algorithm with a uniform power schedule within simulation.