A MULTI-LAYER SWARM CONTROL MODEL FOR INFORMATION PROPAGATION AND MULTI-TASKING

multi-agent control, bio-inspired swarm control, source seeking, level curve tracking, information propagation

Al Abri, Said Salim Hamdan S. ; Zhang, Fumin Electrical and Computer Engineering Egerstedt, Magnus Wardi, Yorai Coogan, Samuel Tao, Molei ; Zhang, Fumin

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: multi-agent control, bio-inspired swarm control, source seeking, level curve tracking, information propagation;   

Others  :  https://smartech.gatech.edu/bitstream/1853/63530/1/ALABRI-DISSERTATION-2019.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Modeling and control of multi-agent systems is an important problem due to its large variety of potential applications and increasing practical and theoretical challenges. A largepart of inspiration for modeling and control of multi-agent systems originates from the study of natural collective behaviors observed for example in schools of fish, flocks ofbirds, colonies of ants and cultures of bacteria. While individuals in these natural swarms are collectively performing complex tasks such as foraging or synchronization, critical information such as predator warnings propagate across the swarm almost instantly and presumably without explicit communication between the individuals.On the other hand, algorithms for multi-agent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing fieldmeasurements among the agents for gradient estimation. The dependence on the exchange of data through a communication channel is a hard requirement that might be undesiredespecially in applications with severe limitations such as underwater robotics.The main contribution of this Dissertation is a Multi-Layer control model composed of an interplay of decentralized algorithms for perception and swarming. In the perceptionlayer, each agent applies a Principal Component Analysis (PCA) on the relative positions and headings of its neighbors to learn principal properties about the motion and the geometryof the spatial distribution of the surrounding agents. These principal components are then used in the swarming layer where various distributed control laws are designed to balance between achieving a collective task and at the same time allowing critical emergingsignals to propagate to the entire swarm.Within this Multi-Layer model, we contributed distributed control laws for swarms to perform collective source seeking and level curve tracking of scalar fields. These controllaws scale to swarms of various sizes and graph structures and do not rely on explicitly estimating the field gradient or explicitly sharing measurements among the agents.Additionally, we contributed a distributed control law that balances between achieving a collective task and at the same time allowing critical signals to propagate to the entire swarm. Through this, we demonstrated implicit information propagation in swarms exhibiting predator-avoidance behavior using only local interactions and without explicit communication or prescribed formations. Moreover, we obtained various stability resultsreflecting the convergence and robustness of the proposed algorithms. Finally, we validated the proposed model for source seeking, level curve tracking and predator avoidancebehaviors through various simulation and experimental results. The proposed control model offers a new method that enables robots with limited resources to perform diverse swarming activities with only local information. Additionally, designing analytical models to understand information propagation will not only reveal natural mysteries but additionally will help to propose multi-tasking control algorithms forrobotic swarms that require only very limited or no explicit communication.