Trajectory Planning for Quadrotor Swarms

By Wolfgang Honig, James A. Preiss, T. K. Satish Kumar, G. Sukhatme, Nora Ayanian. IEEE Transactions on robotics, 2018

316 citations recorded by the source corpus.

We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e02762c1276dbf0b85c58883b523093974492f6e

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Citations in the corpus

Swarm Robotics: Past, Present, and Future (2021, background): “For example, when it is not possible or advisable to coordinate the robots in a centralized way [120], the use of a robot swarm might be the right way to go.”
Geometrically Constrained Trajectory Optimization for Multicopters (2021, background): “As a special case of the problem (11), trajectory generation within 3-dimensional Safe Flight Corridors (SFCs) has been widely adopted in real-world applications such as [31], [63], and [64].”
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses (2020, background): “MAPF has numerous real-world applications, such as autonomous aircraft-towing vehicles (Morris et al. 2016), video game characters (Li et al. 2020b), and quadrotor swarms (H¨onig et al. 2018).”
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks (2019, background): “This may introduce new types of conﬂicts, such as vertex-to-vertex, edge-to-edge, and edge-to-vertex conﬂicts (H¨onig et al. 2018).”