Distributed control enables drone swarms to safely form patterns
Distributed Stochastic Optimal Control for Pattern-Oriented Swarms
Robotics
Summary
Controlling groups of drones to fly in specific shapes is tricky because they need to avoid bumping into each other and moving obstacles, even when conditions change. The authors created a mathematical framework that helps drone swarms plan their movements probabilistically, letting them handle uncertainty and obstacles safely. Their method lets the drones form and keep patterns without assigning specific spots to each drone, allowing the group to heal itself if something goes wrong. They tested this approach both in computer simulations and real drone flights, showing it works well indoors and outdoors with different types of drones.
What this means in practice
- •For drone fleet operators: Coordinate drone teams to safely fly in specific formations while avoiding moving obstacles and adapting to environmental uncertainty.
- •For robotics system integrators: Implement a platform-agnostic control framework supporting diverse unmanned vehicles to maintain patterns and navigate dynamic spaces safely.
Authors
Qingrui Zhang, Chenghao Yu, Feng Xue, Xintong Wang
Abstract
While offering significant promise for diverse applications, pattern-oriented swarms encounter multifaceted challenges in geometric control, self-organization, and safe navigation through dynamic environments. In this paper, we present a GRF-based stochastic optimal control framework to address these challenges within a unified probabilistic architecture. By extending the GRF into the temporal domain, the proposed framework casts collective coordination as a Bayesian inference task, enabling swarms to accommodate environmental uncertainty, satisfy non-convex constraints, and reconcile heterogeneous dynamics across diverse platforms. We develop an uncertainty- and safety-aware collision avoidance module for navigation in the presence of stochastic obstacle motion. The unscented transform is employed to propagate state uncertainty for both dynamic obstacles and neighboring agents, yielding principled confidence bounds for collision avoidance. In addition, density-guided pattern control is introduced, which encodes geometric patterns as implicit density fields. This representation decouples pattern specification from explicit agent-to-target assignments, thereby facilitating intrinsic self-healing and elastic reconfiguration in a distributed manner. The proposed framework is extensively evaluated through Monte Carlo simulations across diverse scenarios. Its model-agnostic nature is demonstrated on both quadrotor and fixed-wing UAV swarms, highlighting its generalizability across platforms with heterogeneous dynamics. Finally, the efficacy and robustness of the proposed method are validated through indoor experiments with a 15-quadrotor swarm and outdoor deployments involving 4 custom-built autonomous quadrotors. These experiments substantiate the proposed framework's capacity to maintain reliable geometric pattern transitions and safety-aware navigation within real-world environments.