AGDS: adaptive goal-directed strategy for swarm drones flying through unknown environments

Abstract

Abstract This paper aims to address a challenging problem of a drone swarm for a specific mission by reaching a desired region, through an unknown environment. A bio-inspired flocking algorithm with adaptive goal-directed strategy (AGDS) is proposed and developed for the drones swarmed across unknown environments. Each drone employs a biological visual mechanism to sense obstacles in within local perceptible scopes. Task information of the destination is only given to a few specified drones (named as informed agents), rather than to all other individual drones (uninformed agents). With the proposed flocking swarm, the informed agents operate collectively with the remaining uninformed agents to achieve a common and overall mission. By virtue of numerical simulation, the AGDS and non-adaptive goal-directed strategy (non-AGDS) are both presented and evaluated. Experiments by flying six DJI Tello quadrotors indoor are conducted to validate the developed flocking algorithm. Additional validations within canyon-like complicated scenarios have also been carried out. Both simulation and experimental results demonstrate the efficiency of the proposed swarm flocking algorithm with AGDS.

Keywords

• Unmanned intelligent cluster
• Drone swarm
• Collective behavior
• Self-organization
• Adaptive goal-directed strategy (AGDS)