Embedded models of UAV swarm control algorithm

Abstract

This paper presents an approach to the embedded system software implementation based on a Model-View-Controller (MVC) architecture, specially modified for the multi-agent domain. It aims to solve the poor scalability and single-point-of-failure risks associated with centralized, cyclic-polling architectures in unmanned aerial vehicle (UAV) swarms. In this novel interpretation, the UAV swarm itself is re-envisioned as the User, a continuously updated state of the Control E-Network (CEN) (token markings and attributes) serves as the View, and the network’s transitions and listeners function as the Controller, manipulating the model. The model itself is expressed as a CEN that is an extension of Petri nets for control purposes. The places and data of CEN represent the agent’s state, but in general, CEN provides a control algorithm view as a set of transitions and their related places to implement event-driven logic. At the same time, listeners integrate external input signals and generate output commands in a fully reactive manner. The resulting chain-driven, event-oriented execution eliminates cyclic polling, reduces CPU overhead, simultaneously supporting synchronous, asynchronous, and parallel event processing in real time. The paper details the Python-based data structures — places, transitions, queues/stacks, thread pool, and reactive listeners, — together with a dynamic verification method that automatically collects statistics on transition activity and timing, enabling on-the-fly profiling and detection of performance bottlenecks. A comprehensive example demonstrates an agent program with a three-layered (reactive, planning, and cooperative) control model that reacts in parallel to sensor events, performs delayed actions, and synchronizes results through a joining transition, thereby confirming the effectiveness of the proposed approach for multi-agent applications.