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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.","lang":"eng"}],"language":[{"iso":"eng"}],"status":"public","date_updated":"2026-09-24T12:22:54Z","date_created":"2026-09-21T14:31:38Z","publication_status":"accepted","author":[{"last_name":"Kirsch","first_name":"André","full_name":"Kirsch, André","id":"229807"},{"first_name":"Jan","orcid":"0000-0002-4579-214X","last_name":"Rexilius","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227345663","full_name":"Rexilius, Jan","id":"245736"}],"_id":"7173","citation":{"apa":"Kirsch, A., &#38; Rexilius, J. (n.d.). Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints. 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