Decentralized aggregation and leader-following control of a swarm of quadcopters with nonlinear under-actuated dynamics

Parrany, Ahmad Mahdian; Alasty, Aria

doi:10.1016/j.ast.2020.106317

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…For the former numerous techniques have been proposed for obstacle-free environments. These approaches encompass virtual structures [5], leader-follower [6] [42], navigation functions [7] [43], reactive behaviors [8], consensus-based control laws [9], and barycentric coordinate-based control [10]. Recent developments include the presentation of a VIO-swarm system [11], capable of executing collision-free formation flight in open space without inter-robot collisions.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Asynchronous Formation Planning of Multirotor Aerial Vehicles in Dynamic Environments Using Flexible Formation Graphs and Tight Trajectory Hulls

Tanveer,

Kadri

2024

IEEE Access

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Formation flight holds significant potential for various applications involving aerial robot swarms. However, current methodologies lack the capability to autonomously execute large-scale formation flights in densely populated environments. To bridge the gap, a decentralized and asynchronous formation flight planner is proposed based on a graph-based formation metric and tight representations of kinodynamically feasible trajectories for collision avoidance. The planner handles formation path planning along with dynamic obstacles and intervehicle collision avoidance using minimum volume convex hulls for agent trajectories. The employed formation metric is invariant to rotation, translation, and scaling, granting greater flexibility in formation coordination. A decoupled and distributed trajectory optimization framework is proposed to enhance the computational feasibility of large-scale formation flights. Moreover, to mitigate issues relating to communication delays between agents, asynchronous execution of a finite horizon navigation framework with usage of sparse trajectory control points for trajectory segments is employed. Simulations with multiple agents, static and dynamic obstacles support the robustness of the planner to formation flights in real world. The planner demonstrates goal/waypoints achievement and formation adherence capabilities that are assessed and compared using a popular quantifiable formation similarity metric. Furthermore, the paper also serves as a guideline to build upon trajectory planning frameworks for tight formation control in cluttered, dynamic environments.

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