A networked aerial robot team (NART) comprises a group of agents (e.g., unmanned aerial vehicles (UAVs), ground control stations, etc.) interconnected by wireless links. Inter-agent connectivity, even if intermittent (i.e. sparse), enables data exchanges between agents and supports cooperative behaviours in several NART missions. It can benefit online decentralised decision-making and group resilience, particularly when prior knowledge is inaccurate or incomplete. These requirements can be accounted for in the offline mission planning stages to incentivise cooperative behaviours and improve mission efficiency during the NART deployment. This paper proposes a novel path planning tool for a Sparse, Aware, and Cooperative Networked Aerial Robot Team (SpArC-NART) in exploration missions. It simultaneously considers different levels of prior information regarding the environment, limited agent energy, sensing, and communication, as well as distinct NART constitutions. The communication model takes into account the limitations of user-defined radio technology and physical phenomena. The proposed tool aims to maximise the mission goals (e.g., finding one or multiple targets, covering the full area of the environment, etc.), while cooperating with other agents to reduce agent reporting times, increase their global situational awareness (e.g., their knowledge of the environment), and facilitate mission replanning, if required. The developed cooperation mechanism leverages soft-motion constraints and dynamic rewards based on the Value of Movement and the expected communication availability between the agents at each time step. A ground sensing coverage use case was chosen to illustrate the current capabilities of this tool.

翻译：网络化空中机器人团队（NART）由一组通过无线链路互连的智能体（例如无人机、地面控制站等）组成。智能体间的连通性——即使是间歇性的（即稀疏的）——也能实现智能体间的数据交换，并在多种NART任务中支持协作行为。这有助于在线分散式决策与群体韧性，尤其是在先验知识不准确或不完整的情况下。这些需求可在离线任务规划阶段加以考虑，以激励协作行为并提升NART部署期间的任务效率。本文提出了一种面向稀疏、感知与协作网络化空中机器人团队（SpArC-NART）在探索任务中的新型路径规划工具。该工具同时考虑了关于环境的不同先验信息水平、有限的智能体能量、传感与通信能力，以及不同的NART构成方式。通信模型考虑了用户自定义无线电技术的限制与物理现象。所提出的工具旨在最大化任务目标（例如，寻找单个或多个目标、完全覆盖环境区域等），同时通过与其他智能体协作来减少智能体报告时间、提升其全局态势感知能力（例如，对环境的知识），并在需要时促进任务重规划。所开发的协作机制利用了基于移动价值与各时间步智能体间预期通信可用性的软运动约束和动态奖励。本文选取地面传感覆盖应用案例以展示该工具当前的能力。