Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases

2026-02-15

AI Summary

This work proposes a path planning framework for collaborative aerial robot teams—SpArC-NART—to address challenges posed by sparse communication, energy constraints, and incomplete environmental priors. The approach integrates multi-level environmental priors, energy limitations, sensing capabilities, and a radio propagation–based communication model. It introduces a dynamic reward mechanism during task planning that jointly considers mobility value and expected communication availability, along with soft motion constraints to enable online replanning. Experimental results demonstrate that the proposed method significantly reduces reporting latency in ground coverage tasks while enhancing global situational awareness, mission efficiency, and system robustness.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Path Planning
• Networked Aerial Robot Teams
• Sparse Connectivity
• Cooperative Robotics
• Ground Sensing Coverage

Innovation

• Path Planning Optimization
• Sparse Connectivity
• Cooperative Robotics
• Dynamic Reward Mechanism
• Networked Aerial Robots