Hybrid Model for A Priori Performance Prediction of Multi-Job Type Swarm Search and Service Missions

Abstract

In Swarm Search and Service (SSS) applications, swarm vehicles are responsible for concurrently searching an area while immediately servicing jobs discovered while searching. Multiple job types may be present in the environment. As vehicles move in and out of the swarm to service jobs, the coverage rate (i.e., area searched by the swarm per time step) changes dynamically to reflect the number of vehicles currently engaged in search. As a result, the arrival rates of jobs also changes dynamically. When planning SSS missions, the resource requirements, such as the swarm size needed to achieve a desired system performance, must be determined. The dynamically changing arrival rates make traditional queuing methods ill-suited to predict the performance of the swarm. This paper presents a hybrid method - Hybrid Model - for predicting the performance of the swarm a priori. It utilizes a Markov model, whose state representation captures the proportion of agents searching or servicing jobs. State-dependent queuing models are used to calculate the state transition function of the Markov states. The model has been developed as a prediction tool to assist mission planners in balancing complex trade-offs, but also provides a basis for optimizing swarm size where cost functions are known. The Hybrid Model is tested in previously considered constant coverage rate scenarios and the results are compared to a previously developed Queuing Model. Additional SSS missions are then simulated and their resulting performance is used to further evaluate the effectiveness of using the Hybrid Model as a prediction tool for swarm performance in more general scenarios with dynamically changing coverage rates.