An integrated decision-making framework of a heterogeneous aerial robotic swarm for cooperative tasks with minimum requirements. (May 2019)

• Record Type: Journal Article
• Authors: Jang, Inmo; Shin, Hyo-Sang; Tsourdos, Antonios; Jeong, Junho; Kim, Seungkeun; Suk, Jinyoung
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 6(2019)
• Page Start: 2101
• Page End: 2118
• Publication Date: 2019-05
• Subjects: Unmanned aerial vehicles -- swarm robotics -- mission planning -- task allocation -- coalition formation -- path planning; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410018772622
• Languages: English
• ISSNs: 0954-4100

Abstract

Given a cooperative mission consisting of multiple tasks spatially distributed, an aerial robotic swarm's decision-making issues include team formation, team-to-task assignment, agent-to-work-position assignment and trajectory optimisation with collision avoidance. The problem becomes even more complicated when involving heterogeneous agents, tasks' minimum requirements and fair allocation. This paper formulates all the combined issues as an optimisation problem and then proposes an integrated framework that addresses the problem in a decentralised fashion. We approximate and decouple the complex original problem into three subproblems (i.e. coalition formation, position allocation and path planning), which are sequentially addressed by three different proposed modules. The coalition formation module based on game theories deals with a max-min problem, the objective of which is to partition the agents into disjoint task-specific teams in a way that balances the agents' work resources in proportion to the task's minimum workload requirements. For agents assigned to the same task, given reasonable assumptions, the position allocation subproblem can be efficiently addressed in terms of computational complexity. For the trajectory optimisation, we utilise a Model Predictive Control and Sequential Convex Programming algorithm, which reduces the size of the problem so that the agents can generate collision-free trajectories on a real-time basis. As a proof of concept, we implement the framework into an unmanned aerial vehicle swarm's cooperative stand-in jamming mission scenario and show its feasibility, fault tolerance and near-optimality based on numerical experiment.