---
_id: '7173'
abstract:
- lang: eng
  text: 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:
- first_name: André
  full_name: Kirsch, André
  id: '229807'
  last_name: Kirsch
- first_name: Jan
  full_name: Rexilius, Jan
  id: '245736'
  last_name: Rexilius
  orcid: 0000-0002-4579-214X
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227345663
citation:
  alphadin: '<span style="font-variant:small-caps;">Kirsch, André</span> ; <span style="font-variant:small-caps;">Rexilius,
    Jan</span>: Reinforcement Learning-based MAV Navigation With Parameterized Waypoints:
    Incorporating Orientation, Speed Limits, and Corridor Constraints. In:'
  ama: 'Kirsch A, Rexilius J. Reinforcement Learning-based MAV Navigation With Parameterized
    Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints.'
  apa: 'Kirsch, A., &#38; 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.'
  bibtex: '@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} }'
  chicago: 'Kirsch, André, and Jan Rexilius. “Reinforcement Learning-Based MAV Navigation
    With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints,” n.d.'
  ieee: '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.'
  mla: 'Kirsch, André, and Jan Rexilius. <i>Reinforcement Learning-Based MAV Navigation
    With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints</i>.'
  short: 'A. Kirsch, J. Rexilius, in: n.d.'
conference:
  end_date: 2026-11-20
  location: Barcelona
  name: 9th Iberian Robotics Conference (ROBOT)
  start_date: 2026-11-18
date_created: 2026-09-21T14:31:38Z
date_updated: 2026-09-21T14:33:10Z
keyword:
- MAV navigation
- Reinforcement learning
- Constrained navigation
- Control barrier functions
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
  name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: 'Reinforcement Learning-based MAV Navigation With Parameterized Waypoints:
  Incorporating Orientation, Speed Limits, and Corridor Constraints'
type: conference
user_id: '229807'
year: '2026'
...