2026/02/15 by Maria Conceição, Maria Inês Conceição, António Grilo +1
Engineering · Computer Science · #UAV Applications and Optimization #Distributed Control Multi-Agent Systems #Robotic Path Planning Algorithms
paper · pdf · doi:10.1016/j.robot.2026.105629
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 Sparse, Aware, and Cooperative NARTs (SpArC-NARTs) 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, including redundant storage for reliability purposes), and facilitate mission replanning, if required. The developed cooperation mechanism leverages soft-motion constraints and dynamic reward shaping based on the Value of Movement and expected communication availability between the agents at each time step. The capabilities of this tool were illustrated with a ground sensing coverage use case. The performance of the proposed mechanism was analysed for different NART constitutions and task distributions. Their performance was compared to one non-cooperative baseline and two cooperative baselines. An ablation study to examine the impact of reward components and connectivity requirements on NART performance and an analysis of the impact of weighting UAV tasks (exploration and reporting) differently were also performed.