With the advent of intelligent transport, quadrotors are becoming an attractive solution while lifting or dropping payloads during emergency evacuations, construction works, etc. During such operations, dynamic variations in (possibly unknown) payload cause considerable changes in the system dynamics. However, a systematic control solution to tackle such varying dynamical behaviour is still missing. In this work, two control solutions are proposed to solve two specific problems in aerial transportation of payload, as mentioned below. In the first work, we explore the tracking control problem for a six degrees-of-freedom quadrotor carrying different unknown payloads. Due to the state-dependent nature of the uncertainties caused by variation in the dynamics, the state-of-the-art adaptive control solutions would be ineffective against these uncertainties that can be completely unknown and possibly unbounded a priori. In addition, external disturbances such as wind while following a trajectory in all three positions and attitude angles. Hence, an adaptive control solution for quadrotors is proposed, which does not require any a priori knowledge of the parameters of quadrotor dynamics and external disturbances. The second work is focused on the interchanging dynamic behaviour of a quadrotor while loading and unloading different payloads during vertical operations. The control problem to maintain the desired altitude is formulated via a switched dynamical framework to capture the interchanging dynamics of the quadrotor during such vertical operations, and a robust adaptive control solution is proposed to tackle such dynamics when it is unknown. The stability of the closed-loop system is studied analytically and the effectiveness of the proposed solution is verified via simulations.
A Morphing Quadrotor that Can Optimize Morphology for Transportation
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Urban Robotics Lab, School of Electrical Engineering, KI-AI, KI-R, KAIST（韩国科学技术院城市机器人实验室）
Feasible Force Set Shaping for a Payload-Carrying Platform Consisting of Tiltable Multiple UAVs Connected Via Passive Hinge Joints
National Institute of Advanced Industrial Science（国家工业科学与技术研究院）；Institute of Science Tokyo, Tokyo, Japan（东京科学研究所）；Kyoto University（京都大学）；The Polytechnic University of Japan（日本工业大学）
Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science（罗伯特·博世网络物理系统中心，印度科学研究院）；Department of Mechanical Engineering, Indian Institute of Science（机械工程系，印度科学研究院）
Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision
School of Aeronautic Science and Engineering, Beihang University（北京航空航天大学航空科学与工程学院）；Research Institute for Frontier Science, Beihang University（北京航空航天大学前沿科学研究院）；School of Electronic and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）
Controller Design and Implementation of a New Quadrotor Manipulation System
Mechatronics and Robotics Engineering, E-JUST（机电一体化与机器人工程，E-JUST）；Systems and Control Engineering, Keio University（系统与控制工程，庆应大学）；Mechanical Engineering, Alexandria University（机械工程，亚历山大大学）；Automatic Control Engineering, Cairo University（自动控制工程，开罗大学）
Design and Control of an Actively Morphing Quadrotor with Vertically Foldable Arms
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University and State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis（自动化与智能感知学院，上海交通大学；航空集成与航空系统-系统综合国家重点实验室）
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Department of Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人系）；Department of Computer Science, Aalto University（艾尔沃斯大学计算机科学系）
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
National Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统国家重点实验室）
Cooperative Control of Multi-Quadrotors for Transporting Cable-Suspended Payloads: Obstacle-Aware Planning and Event-Based Nonlinear Model Predictive Control
Toronto Metropolitan University（多伦多都会大学）；University of Alberta（阿尔伯塔大学）
Physics-Informed Neural Network for Multirotor Slung Load Systems Modeling
Institute for Systems and Robotics (ISR)（系统与机器人研究所）；Laboratory of Robotics and Engineering Systems (LARSyS)（机器人与工程系统实验室）；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；CTS/Uninova（新大学中心技术与系统研究中心）；NOVA School of Science and Technology (FCT-NOVA)（NOVA科学与技术学院）
