Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

Flocking with General Local Interaction and Large Population

This paper studies a flocking model in which the interaction between agents is described by a general local nonlinear function depending on the distance between agents. The existing analysis provided sufficient conditions for flocking under an assumption imposed on the system’s closed-loop states; however this assumption is hard to verify. To avoid this kind of assumption the authors introduce some new methods including large deviations theory and estimation of spectral radius of random geometric graphs. For uniformly and independently distributed initial states, the authors establish sufficient conditions and necessary conditions for flocking with large population. The results reveal that under some conditions, the critical interaction radius for flocking is almost the same as the critical radius for connectivity of the initial neighbor graph.