Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Contemporary approaches to swarm guidance and control often assume that swarm agents are homogeneous in their response to external influence vectors. This manifests in the design of control algorithms, such as herding, often operating directly on the raw positional data of swarm agents to compute influence vectors. Herding-based models, such as shepherding, have been implemented for over 25 years, with classic control methods typically operating on simple transformations of raw data Hasan, Baxter, Castillo, Delgado, and Tapia (2022). Swarm shepherding is an example of a swarm control herdingbased method where one or more external actuators (sheepdogs) operate on low-level information by calculating primitive statistical features from raw data. These models often use static behaviour selection policies for the control agent to guide a swarm to a goal location Debie et al. (2021). As a biologically-inspired approach to swarm control, shepherding has applications across different domains, such as the guidance and control of crowds Li, Hu, Liang, and Li (2012), herding biological animals Paranjape, Chung, Kim, and Shim (2018), guiding teams of uncrewed system (UxS) Hepworth (2021), and controlling a group of robotic platforms Cowling and Gmeinwieser (2010); Lee and Kim (2017).