Hybrid Model for A Priori Performance Prediction of MultiJob Type Swarm Search and Service Missions 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 illsuited 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. Statedependent 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 tradeoffs, 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.

Analysis of Random Walk Models in Swarm Robots for Area Exploration The purpose of area exploration is to cover an area effectively and swarm robots are used widely for this type of area exploration because of their robustness, flexibility, and scalability. Meanwhile, due to the limited individual abilities of swarm robots, the random walk methods have been the generally used areaexploration strategy. Although random walk methods possess better performance in area exploration, there still exist some problems. For one thing, little work has been devoted to the theoretical analysis of the random walk models. For another, no effective measures are used to evaluate the searching efficiency of random walk methods and the searching efficiency is verified mainly by simulation experiments. Therefore, in order to make up for the deficiency, this paper presents the mathematical description of the random walk models by drawing on the experience of related researches in biology. The proposed mathematical theory can not only be used to aid our understanding of random walks but also be easy to analyze and control random motion of the robots. Also, the mean squared displacement (MSD) is introduced as the performance measure to evaluate the effectiveness of the random walk methods. In order to proof the effectiveness of the performance measure of MSD, the areaexploration missions of swarm robots are carried out in the simulation experiments and the the coverage rate is used to evaluate the searching efficiency. The experimental results prove that the MSD is an effective performance measure to evaluate the searching efficiency.