Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller - Chang, Yu-Cheng; Dostovalova, Anna; Lin, Chin-Teng; Kim, Jijoong (Frontiers in Artificial Intelligence, 2020)
We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents for simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning LiDAR to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronisation of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. Parameters of all the controllers are optimised using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware which will follow in the next phase of our research.