Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints

conference paper Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints accepted yes André Kirsch author 229807 Jan Rexilius author 2457360000-0002-4579-214X 9th Iberian Robotics Conference (ROBOT) Institute for Building Intelligence project 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. 2026Barcelona eng MAV navigationReinforcement learningConstrained navigationControl barrier functions A. Kirsch, J. Rexilius, in: n.d. A. Kirsch and J. Rexilius, “Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints,” presented at the 9th Iberian Robotics Conference (ROBOT), Barcelona. Kirsch, André, and Jan Rexilius. Reinforcement Learning-Based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints. Kirsch, André ; Rexilius, Jan: Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints. In: Kirsch A, Rexilius J. Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints. @inproceedings{Kirsch_Rexilius, title={Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints}, author={Kirsch, André and Rexilius, Jan} } Kirsch, A., & Rexilius, J. (n.d.). Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints. Presented at the 9th Iberian Robotics Conference (ROBOT), Barcelona. Kirsch, André, and Jan Rexilius. “Reinforcement Learning-Based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints,” n.d. 71732026-09-21T14:31:38Z2026-09-21T14:33:10Z