Cognitive Robotics: Enhancing Multirobot Target Search in Unknown Environments Through Adaptive Communication Strategies
提出一种自适应通信分层分布式模型预测控制框架，用于多机器人在未知环境中搜索多个目标，显著降低时间和通信成本，提高任务成功率。
This article presents a novel approach to improving multitarget searching in unknown environments using multirobot systems while ensuring adaptability to changing communication conditions. The proposed method addresses challenges arising from limited scope, dynamic circumstances, and inaccurate decision data due to communication disruptions or interference in real-world scenarios. A comprehensive environmental map is generated using a grid-based mapping methodology, encompassing data related to obstacles, coverage, target occupancy, and communication conditions. Considering the constraints imposed by communication conditions, we develop the adaptive communication condition hierarchical distributed model predictive control framework. This framework incorporates a hierarchical communication strategy for multirobot target search. To assess the effectiveness of our approach, a series of comparative experiments are conducted on three distinct maps, each characterized by unique communication environments, obstacle layouts, and target distributions. These experiments employ four commonly used swarm intelligence algorithms. The research findings indicate that implementing the proposed search framework and communication strategy significantly reduces the time and communication costs associated with locating targets in complex and unfamiliar environments. This is particularly relevant for multirobot systems operating under diverse and limited communication conditions, substantially increasing the task’s success rate.