Stag hunt game-based approach for cooperative UAVs

TL;DR

This paper introduces a cooperative UAV path planning method using stag hunt game theory combined with particle swarm optimization, enhancing multi-UAV task efficiency in construction site inspections.

Contribution

It develops a novel multi-UAV path planning algorithm based on stag hunt game theory and PSO, addressing collaboration and efficiency in construction-related UAV tasks.

Findings

Effective generation of feasible UAV paths during inspection tasks.

Improved task efficiency demonstrated in simulation results.

Framework applicable to large construction sites.

Abstract

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology has found applications in numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant equilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.