arXiv (OAI Expanded)
open access
Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors
Agile flights of autonomous quadrotors in cluttered environments require constrained motion planning and control subject to translational and rotational dynamics. Traditional model-based methods typically demand complicated design and heavy computation. In this paper, we develop a novel deep reinfor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Efficient robot navigation inspired by honeybee learning flights.#239607
- FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite#973656
- Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models#1144918
- Space Drones: An Opportunity to Include, Engage, Accelerate, and Advance#364404
- QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation#134584
Record · ID 1070646
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive