Multi-quadcopter team leader path planning using particle swarm optimization

Zihao Liang, Hossein Rastgoftar, Ella M. Atkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Path planning for multi-agent systems (MAS) is a challenging problem. This paper describes a novel method that utilizes particle swarm optimization to optimize trajectories of the leaders of a multi-quadcopter system (MQS) in a static and known environment. MQS collective motion in Rn (n = 1, 2, 3) is based on a leader-follower paradigm inspired by continuum mechanics. Under this paradigm, the MQS can deform under a homogeneous mapping. This homogeneous transformation in Rn is uniquely defined by the trajectories of n+1 leaders placed at the vertices of a deformable polytope called the leading polytope. With planned leader trajectories, the deformation can be acquired by the followers through local communication with n+1 nearby quadcopters. The proposed hierarchical path planning method consists of global path planning and local trajectory planning for MAS leaders. First, a collision-free roadmap, specifically a generalized Voronoi diagram, is generated. A well-established A* search procedure identifies an optimal route through this map. Waypoints on this optimal path are then passed into a particle swarm optimization (PSO) formulation that generates smooth trajectories for MQS leaders. The proposed method ensures collision avoidance and quadcopter containment. Simulation results demonstrate the proposed method.

Original languageEnglish (US)
Title of host publicationAIAA Aviation 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Number of pages14
ISBN (Print)9781624105890
StatePublished - 2019
Externally publishedYes
EventAIAA Aviation 2019 Forum - Dallas, United States
Duration: Jun 17 2019Jun 21 2019

Publication series

NameAIAA Aviation 2019 Forum


ConferenceAIAA Aviation 2019 Forum
Country/TerritoryUnited States


  • Cooperative control
  • Multi-agent systems
  • Optimization
  • Path planning
  • Unmanned aircraft systems

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Aerospace Engineering


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