@inproceedings{7173,
  abstract     = {Reinforcement learning has achieved state-of-the-art performance in MAV control, waypoint flight, and obstacle avoidance. However, existing RL approaches often assume fixed objectives and constraints, with flight behavior largely limited by vehicle dynamics and orientation considered only when required for locomotion. Classical planning and model predictive control handle such constraints explicitly, but require optimization or replanning. This motivates methods that combine learned local control with explicit constraint handling. We combine reinforcement learning with control barrier functions to improve constraint-aware execution. We propose parameterized waypoints that encode orientation, velocity, and corridor constraints. Simulation and real-world experiments show that a single policy can execute different constraint-parameterized navigation scenarios, revealing scenario-dependent trade-offs between traversal time, tracking accuracy, and constraint satisfaction.},
  author       = {Kirsch, André and Rexilius, Jan},
  keywords     = {MAV navigation, Reinforcement learning, Constrained navigation, Control barrier functions},
  location     = {Barcelona},
  title        = {{Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints}},
  year         = {2026},
}

@inproceedings{6790,
  author       = {Kirsch, André and Rexilius, Jan},
  keywords     = {Waste monitoring, Waste level estimation, MAV navigation},
  location     = {Lissabon, Portugal},
  title        = {{Vision-Based Autonomous Waste Bin Fill-Level Monitoring with a Micro Aerial Vehicle}},
  doi          = {10.1109/IE69249.2026.11539031},
  year         = {2026},
}

@inproceedings{5771,
  author       = {Kirsch, André and Rexilius, Jan},
  booktitle    = { Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods },
  editor       = {Castrillon-Santana, Modesto  and De Marsico, Maria and Fred, Ana },
  isbn         = { 978-989-758-730-6},
  issn         = {2184-4313},
  keywords     = {Tracking, Robot, Drone, MAV, External, Time-of-Flight, LiDAR},
  location     = {Porto},
  publisher    = {Science and Technology Publications},
  title        = {{ An Easy-to-Use System for Tracking Robotic Platforms Using Time-of-Flight Sensors in Lab Environments}},
  doi          = {10.5220/0013110500003905},
  year         = {2025},
}