---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: André
      foaf_name: Kirsch, André
      foaf_surname: Kirsch
      foaf_workInfoHomepage: http://www.librecat.org/personId=229807
  - foaf_Person:
      foaf_givenName: Jan
      foaf_name: Rexilius, Jan
      foaf_surname: Rexilius
      foaf_workInfoHomepage: http://www.librecat.org/personId=245736
    orcid: 0000-0002-4579-214X
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227345663
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_subject:
  - MAV navigation
  - Reinforcement learning
  - Constrained navigation
  - Control barrier functions
  dct_title: 'Reinforcement Learning-based MAV Navigation With Parameterized Waypoints:
    Incorporating Orientation, Speed Limits, and Corridor Constraints@'
...