A Multi-Mode Sequential Flocking Strategy for Motion Control of Multiple Mobile Robots in Obstacle Environment

Although it is relative new, flocking control has been widely attended to coordinates the motions among multiple agents or robots stably based on potential principles. To provide a generic and efficient solution to the local minimum problem encountered within various flocking systems in unexpected and irregular obstacle environment, this paper presents a new design of a multi-model flocking control for multiple mobile robots in terms of behavior-based robotics. Within this framework, some animal-imitating behavior modes are integrated with the new sequential flocking strategy. In this way, the unity of stable aggregation in flocking and efficient obstacle avoidance motion can be efficiently and simultaneously achieved. The simulation studies are comprehensively conducted to demonstrate the efficacy of the proposed procedure.