Robust RL–SMC Hierarchical Control for Leader–Follower Quadrotor Swarms in 3D Obstacle-rich Environments
2026 18th International Workshop on Variable Structure Systems (VSS), Exeter, İngiltere, 27 - 30 Temmuz 2026, ss.1-6, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/vss69650.2026.11655825
- Basıldığı Şehir: Exeter
- Basıldığı Ülke: İngiltere
- Sayfa Sayıları: ss.1-6
- Kocaeli Üniversitesi Adresli: Evet
Özet
Coordinated quadrotor swarms operating in cluttered environments must
simultaneously achieve robust low-level stabilization and high-level,
constraint-aware motion generation for safe formation flight. This paper
proposes a hierarchical RL-SMC control framework for leader–follower
quadrotor swarms navigating three-dimensional obstacle-rich workspaces
that include explicit no-fly regions. At the low level, a boundary-layer
sliding mode attitude controller provides disturbance-tolerant tracking
of commanded Euler references, while a mid-level translational loop
converts bounded velocity commands into physically feasible thrust and
attitude setpoints. At the high level, a reinforcement learning (RL)
policy learns to generate bounded planar acceleration references for the
leader, using an observation and reward design that explicitly accounts
for both the leader state and the worst-case clearance of the formation
footprint to obstacles and restricted zones. The followers track
leader-aligned V-shape offsets, enabling formation motion without direct
learning-based actuation. Simulation results in a constrained 3D
environment demonstrate goal-reaching while maintaining formation
integrity and satisfying safety margins, with smooth bounded guidance
actions and no collisions or no-fly violations. The proposed
architecture combines the adaptability of RL with the robustness
properties of variable-structure control in a modular, safety-oriented
swarm control stack.