The Group Movement Optimization of Autonomous Agents in a Locally-Centric Navigation Model

The scope of autonomous robotic systems capable of performing tasks without global positioning is rapidly expanding. This study aims to develop algorithms for optimal control of autonomous agent groups in a relative coordinate system using local information (pairwise distances and angles) without relying on global positioning. Novel formulations of optimization problems for agent group control are proposed, and computer simulations for 100 agents are conducted. A set of criteria and metrics is introduced to evaluate algorithm performance, enabling conclusions about their applicability and alignment with simulation requirements. Based on the analysis of group control methods and environmental dynamics, recommendations are provided for centralized and decentralized approaches, as well as formation-based or cloud-like motion. A hybrid algorithm combining the potential field method and particle swarm optimization is proposed, achieving balanced motion characteristics for agent groups.