Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

With the emergence of swarm intelligent systems, especially the swarming of aircraft and ground vehicles, cooperation in multiple dimensions has becoming one of the great challenges. How to dynamically schedule the resources within a swarm intelligent system and optimize the execution of tasks are all vital aspects for such systems. Focusing on this topic, in this paper, one new task planning mechanism with multiple constraints is proposed to solve such dynamic programming problems. Concretely, several fundamental models, covering three-level task models and resource-service pool models, are put forward and defined first. Considering the limitations of swarm systems running within complicated cyber-physical space, multi-dimension constraints for tasks scheduling and execution are further modeled and established. On this basis, we mapped this planning problem to an optimization searching problem, and then proposed a Genetic-Algorithm-based mechanism. All these works have been verified with simulated cooperation scenes. Experimental results show that this new mechanism is efficient to solve such resource-related and mission-oriented cooperation problems in complicated environments.