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First-order reinforcement learning with differentiable simulation is promising for quadrotor control, but practical progress remains fragmented across task-specific settings. To support more systematic development and evaluation, we present…

机器人学 · 计算机科学 2026-03-24 Fanxing Li , Fangyu Sun , Tianbao Zhang , Shuyu Wu , Dexin Zuo , yufei Yan , Wenxian Yu , Danping Zou

Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem…

机器人学 · 计算机科学 2024-01-10 Guanrui Li , Alex Tunchez , Giuseppe Loianno

Agile maneuvering of the quadrotor cable-suspended system is significantly hindered by its non-smooth hybrid dynamics. While model-free Reinforcement Learning (RL) circumvents explicit differentiation of complex models, achieving…

机器人学 · 计算机科学 2026-03-12 Dongcheng Cao , Jin Zhou , Shuo Li

Traditional learning approaches proposed for controlling quadrotors or helicopters have focused on improving performance for specific trajectories by iteratively improving upon a nominal controller, for example learning from demonstrations,…

系统与控制 · 计算机科学 2016-10-20 Somil Bansal , Anayo K. Akametalu , Frank J. Jiang , Forrest Laine , Claire J. Tomlin

In this paper, we prove that the dynamical model of a quadrotor subject to linear rotor drag effects is differentially flat in its position and heading. We use this property to compute feed-forward control terms directly from a reference…

机器人学 · 计算机科学 2018-03-29 Matthias Faessler , Antonio Franchi , Davide Scaramuzza

This paper presents an equivariant reinforcement learning framework for quadrotor unmanned aerial vehicles. Successful training of reinforcement learning often requires numerous interactions with the environments, which hinders its…

机器学习 · 计算机科学 2023-02-28 Beomyeol Yu , Taeyoung Lee

Multi-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of…

机器人学 · 计算机科学 2024-05-17 Pascal Goldschmid , Aamir Ahmad

By leveraging the underlying structures of the quadrotor dynamics, we propose multi-agent reinforcement learning frameworks to innovate the low-level control of a quadrotor, where independent agents operate cooperatively to achieve a common…

机器人学 · 计算机科学 2024-02-28 Beomyeol Yu , Taeyoung Lee

Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to develop recovery strategies so that the vehicle is not…

机器人学 · 计算机科学 2021-09-23 Paras Sharma , Prithvi Poddar , P. B. Sujit

Multirotors play a significant role in diverse field robotics applications but remain highly susceptible to actuator failures, leading to rapid instability and compromised mission reliability. While various fault-tolerant control (FTC)…

机器人学 · 计算机科学 2025-05-14 Dohyun Kim , Jayden Dongwoo Lee , Hyochoong Bang , Jungho Bae

The ability of aerial robots to operate in the presence of failures is crucial in various applications that demand continuous operations, such as surveillance, monitoring, and inspection. In this paper, we propose a fault-tolerant control…

机器人学 · 计算机科学 2023-09-27 Jennifer Yeom , Guanrui Li , Giuseppe Loianno

Recent quadrotors have transcended conventional designs, emphasizing more on foldable and reconfigurable bodies. The state of the art still focuses on the mechanical feasibility of such designs with limited discussions on the tracking…

系统与控制 · 电气工程与系统科学 2024-06-04 Karishma Patnaik , Wenlong Zhang

In real world scenarios, due to environmental or hardware constraints, the quadrotor is forced to navigate in pure inertial navigation mode while operating indoors or outdoors. To mitigate inertial drift, end-to-end neural network…

机器人学 · 计算机科学 2025-02-26 Shira Massas , Itzik Klein

In recent times, reinforcement learning has produced baffling results when it comes to performing control tasks with highly non-linear systems. The impressive results always outweigh the potential vulnerabilities or uncertainties associated…

机器人学 · 计算机科学 2023-11-14 Arshad Javeed

This paper addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods relied on accurate knowledge of the environment, including the…

机器人学 · 计算机科学 2023-07-03 Yuhan Xie , Minghao Lu , Rui Peng , Peng Lu

This paper proposes a solution to the attitude tracking problem for a novel quadrotor tailsitter unmanned aerial vehicle called swiveling biplane quadrotor. The proposed vehicle design addresses the lack of yaw control authority in…

系统与控制 · 电气工程与系统科学 2019-07-22 Nidhish Raj , Ravi Banavar , Abhishek , Mangal Kothari

In order to further expand the flight envelope of quadrotors under actuator failures, we design a nonlinear sensor-based fault-tolerant controller to stabilize a quadrotor with failure of two opposing rotors in the high-speed flight…

机器人学 · 计算机科学 2020-10-28 Sihao Sun , Xuerui Wang , Qiping Chu , Coen de Visser

Designing robust controllers for precise trajectory tracking with quadrotors is challenging due to nonlinear dynamics and underactuation, and becomes harder with flexible cable-suspended payloads that add degrees of freedom and hybrid…

机器人学 · 计算机科学 2025-10-02 Mintae Kim , Jiaze Cai , Koushil Sreenath

Quadrotors with large thrust-to-weight ratios are able to track aggressive trajectories with sharp turns and high accelerations. In this work, we develop a search-based trajectory planning approach that exploits the quadrotor…

机器人学 · 计算机科学 2017-10-10 Sikang Liu , Kartik Mohta , Nikolay Atanasov , Vijay Kumar

Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine…

机器人学 · 计算机科学 2024-10-15 Kong Yao Chee , Pei-An Hsieh , George J. Pappas , M. Ani Hsieh