Aerial Continuum Manipulation: A New Platform for Compliant Aerial Manipulation

Aerial manipulators can manipulate objects while flying, allowing them to perform tasks in dangerous or inaccessible areas. Advanced aerial manipulation systems are often based on rigid-link mechanisms, but the balance between dexterity and payload capacity limits their broader application. Combining unmanned aerial vehicles with continuum manipulators emerges as a solution to this trade-off, but these systems face challenges with large actuation systems and unstable control. To address these challenges, we propose Aerial Elephant Trunk, an aerial continuum manipulator inspired by the elephant trunk, featuring a small-scale quadrotor and a dexterous, compliant tendon-driven continuum arm for versatile operation in both indoor and outdoor settings. We develop state estimation for the quadrotor using an Extended Kalman Filter, shape estimation for the continuum arm based on piecewise constant curvature, and whole-body motion planning using minimum jerk principles. Through comprehensive fundamental verifications, we demonstrate that our system can adapt to various constrained environments, such as navigating through narrow holes, tubes, or crevices, and can handle a range of objects, including slender, deformable, irregular, or heavy items. Our system can potentially be deployed in challenging conditions, such as pipeline maintenance or electricity line inspection at high altitudes.

Torch relay is an important tradition of the Olympics and heralds the start of the games. Robots applied in the torch-relay activity can not only demonstrate the technological capability of humans to the world but also provide a sight of human lives with robots in the future. This article presents an aerial manipulator designed for the robot-to-robot torch-relay task of the Beijing 2022 Winter Olympics. This aerial manipulator system is composed of a quadrotor, a 3 degree of freedom (DoF) manipulator, and a monocular camera. This article primarily describes the system design and system control scheme of the aerial manipulator. The experimental results demonstrate that it can complete a robot-to-robot torch-relay task under the guidance of vision in the ice and snow field.

The proximity between the multirotor blades and the environmental obstacles restricts the application of aerial manipulators in inspection tasks due to the risk of impacts, the limitation in the reach of the arm, and the physical interactions. This paper presents a long reach aerial manipulator consisting of a hexarotor platform equipped with a 2-DOF compliant joint arm attached at the tip of a one-meter-length link in passive pendulum configuration. The arm integrates magnetic encoders for force/torque estimation-control based on joint deflection, a range sensor in the forearm link for measuring the distance to the contact point, and a camera for visual inspection. A 2-DOF wearable exoskeleton interface has been developed, allowing the teleoperation of the arm with visual feedback in a more intuitive way. The paper also covers the kinematics and dynamics of the aerial manipulator, including the dynamics of the flexible long reach link. The developed system has been evaluated in test-bench and in outdoor flight tests.

Unmanned aerial vehicles (UAVs), when paired with an onboard camera, have proven to be useful tools in many applications, including aerial photography, precision agriculture, and search and rescue operations. Likewise, UAVs capable of physically interacting with the environment have shown great potential to help people perform dangerous, or time-consuming tasks more safely and efficiently than they could on their own. However, due to onboard computation and battery life limitations and complex flight dynamics, using UAVs to physically interact with the environment is still a developing area of research. Considering these limitations, the primary goals of this work are to (1) develop a new UAV platform for aerial manipulation, (2) develop modular hardware and software for the platform to enable specific tasks to be performed autonomously, and (3) develop a visual target tracking method to enable robust performance of autonomous aerial manipulation tasks in unstructured, real-world environments. To that end, the design of the Interacting-BoomCopter UAV (I-BC) is presented here as a new platform for aerial manipulation. With a simple tricopter frame, a single additional actuator for generating horizontal forces, and lightweight, modular end-effectors, the I-BC aims to balance efficiency and functionality in performing aerial manipulation tasks, and is able to perform various tasks such as mounting sensors in hard-to-reach places, and opening small doors or panels. An onboard camera, force and distance sensors, and a powerful single board computer (SBC) enable the I-BC to operate autonomously in unstructured environments, with potential applications in areas such as large-scale infrastructure inspection, industrial inspection and maintenance, and nuclear decontamination efforts.

The aerial manipulator (AM) is a systematic operational robotic platform in high standard on algorithm robustness. Directly deploying the algorithms to the practical system will take numerous trial and error costs and even cause destructive results. In this paper, a new modular simulation platform is designed to evaluate aerial manipulation related algorithms before deploying. In addition, to realize a fully autonomous aerial grasping, a series of algorithm modules consisting a complete workflow are designed and integrated in the simulation platform, including perception, planning and control modules. This framework empowers the AM to autonomously grasp remote targets without colliding with surrounding obstacles relying only on on-board sensors. Benefiting from its modular design, this software architecture can be easily extended with additional algorithms. Finally, several simulations are performed to verify the effectiveness of the proposed system.

With the development of rotor aerial robot, it has been widely used as a stabilized flying platform to achieve observation, communication relay and so on by equipping corresponding equipment, which are always called passive tasks. However, many applications need rotor aerial robots to possess more active capability, such as grasp, carry and assemblage. Thus, the demand for an aerial manipulation system gradually appears, and becomes a hot research issue in recent years. Aerial manipulator system, usually composed of rotor aerial robot and the manipulator, is a new type of robot system with active operating capability. Compared with the mobile manipulator based on the ground and underwater mobile robot which is now widely applied, the aerial manipulation has lager working range and better flexibility obviously. Unfortunately, the new aerial manipulation system also brings new challenges, such as strong dynamics coupling, the modeling and stabilization problem, etc. which may affect its further application. Thus, in our work, an actual physical aerial manipulator system is designed and implemented for its system and application research. Based on the system, the preliminary modeling and control was researched and also an experiment for grasping and carry application is conducted. The conclusions of these researches will help us to realize and improve the system in the future.

A key challenge in the use of drones for an aerial manipulation task such as cutting tree branches is the control problem, especially in the presence of an unpredictable and nonlinear environment. While prior work focused on simplifying the problem by modeling a simple interaction with branches and controlling the system with nonlinear and non-robust control schemes, the current work deals with the problem by designing novel robust nonlinear controllers for aerial manipulation systems that are appropriate for vegetation sampling. In this regard, two different potential control schemes are proposed: nonlinear disturbance observer-based control (NDOBC) and adaptive sliding mode control (ASMC). Each considers the external disturbances and unknown parameters in controller design. The proposed control scheme in both methods employs a decoupled architecture that treats the unmanned aerial vehicle and the manipulator arm of the sampler payload as separate units. In the proposed control structures, controllers are designed after comprehensively investigating the dynamics of both the aerial vehicle and the robotic arm. Each system is then controlled independently in the presence of external disturbances, unknown parameter changes, and the nonlinear coupling between the aerial vehicle and robotic arm. In addition, fully actuated and underactuated aerial platforms are examined, and their stability and controllability are compared so as to choose the most practical framework. Finally, the simulation findings verify and compare the performance and effectiveness of the proposed control strategies for a custom aerial manipulation system that has been designed and developed for field trials.

This paper is concerned with the motion control problem of an aerial manipulator that consists of a quadrotor base and a manipulator. We propose a novel control strategy that incorporates a simplified model considering the critical dynamics, and a linear-MPC-based trajectory converter to ensure precise velocity tracking for the end-effector. The proposed method is a full-body control approach that coordinates both the quadrotor base and the manipulator, ensuring seamless motion execution across the entire system. Furthermore, the method requires only a small set of dynamic model parameters, making it easy to deploy in practice. The method is validated through motion response experiments on a custom-designed aerial manipulator platform. Experimental results show that the platform performs well in outdoor environments, and the control strategy effectively suppresses angular velocity disturbances while coordinating the full-body joints for end-effector motion tasks.

For aerial manipulators, a steady flight must be guaranteed to perform safe interaction with the surrounding environment. This paper focuses on the development of a position control algorithm for an aerial manipulator system (AMS). The position control algorithm is based on the Immersion and Invariance (I&I) theory. The proposed controller maintains the position of the aerial manipulator at the desired point under external and internal disturbances. The control architecture uses a Visual-SLAM technique using on-board sensors for AMS positioning. A series of outdoor experimental tests are performed to demonstrate the effectiveness of the proposed control strategy.

An aerial manipulator system consisting of a quadrotor UAV and a one-DOF manipulator is introduced for contacting operations in the air. As the system is interacting with external environment to do some operations, there will exist contact force. According to the practical aerial contact manipulation features, it is known that an aerial manipulator usually performs operations as the UAV platform is in hover flight or nearby. To realize controlling the force, the hovering UAV acting as a spring-mass-damper system is firstly proved. Then, based on the derived model and the impedance control algorithm, a contact force control method is presented. That is, the position error is controlled in order to control the contact force ultimately. The actual flight experiments show that the developed system is enable to realize the contact force control without a force sensor.

A multi-finger robot system for adaptive landing gear and aerial manipulation

This paper is devoted to a system for aerial manipulation, composed of a helicopter and an industrial manipulator. The usage of an industrial manipulator is motivated by practical applications which were identified in different cooperation projects with the industry. We address the coupling between manipulator and helicopter and show that even in case when we have an ideal controller for manipulator and a highperformance controller for helicopter, an unbounded energy flow can be generated by internal forces between helicopter and manipulator if both controllers are used independently. To solve this problem we propose a new kinematical coupling for control by introducing an additional manipulation DoF realized by helicopter rotation around its yaw axis. The new experimental setup and required modifications in the manipulator controller for this purpose are described. Further, we propose dynamical coupling which is implemented by modification of the helicopter controller feeding the interaction force/torque, measured between manipulator base and fuselage, directly to the actuators of the rotor blades. At the end, we present experimental results for aerial manipulation and their analysis.

This thesis focuses on a novel aerial robot system composed of an unmanned aerial vehicle (UAV) and one or more robotic arms, which is employed for aerial manipulation tasks. In particular, this research tries to transform UAVs from a predominantly data gathering platform into a flying robot, frequently called as unmanned aerial manipulators (UAMs), that can physically interact with the object or surrounding environment. This is challenging due to the existing design limitations imposed by the attachment of robotic arms to floating platforms. Furthermore, adding on to the cross-coupled dynamics of the UAV platform and arms, the presence of strong nonlinearities, external disturbances (e.g., wind), and parameter variations further complicates the deployment of UAMs in day-to-day life. Moreover, having such redundant aerial robots, especially when employing two manipulators, imposes additional challenges to successfully perform UAM missions. Motivated by these challenges, the main contributions of this thesis are threefold. Firstly, we propose a novel design of the dual-arm manipulator specifically developed for UAM missions. The dual-arm system is designed such that it can be attached to different multirotors with minimum design modifications. In addition, the proposed dual-arm design employs prismatic joints to introduce three distinctive features: 1) ability of each arm to dynamically adjust its COG for better flight performance; 2) extended workspace and reach of the arms for enhancing operational capability and improving safety during UAM missions; 3) fully independent control of each arm for performing different tasks simultaneously. Secondly, we present a learning-based intelligent control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy neural networks, for the control of UAMs under time-varying working conditions. The proposed control strategy eliminates the need for precise tuning of conventional controllers, and it can compensate for the internal and external disturbances caused by the motion of the arms and unforeseen environmental changes, opening the door for the widespread use of UAMs in daily life. Thirdly, we propose two different trajectory generation with redundancy resolution strategies for UAMs. The first strategy is based on a weighted damped least-squares method, while the second approach employs a nonlinear model predictive control-based technique. The proposed approaches are capable to exploit the system redundancy by modifying the UAM configuration and simultaneously generate the feasible trajectories during the execution of the assigned tasks while enforcing the system constraints. The contributions mentioned above are evaluated throughout extensive simulation studies and experimental flight tests with real UAM platforms. Overall, these contributions aim for extending the autonomous functionalities and operational capabilities of UAMs in the transportation and assembly type of scenarios, with the ambition to assist lessening the current gaps that hinder the widespread deployment of aerial robotic workers in real-world applications.

Parallel manipulators with less than six degrees of freedom (DOF) have been increasingly used in high-speed hybrid machine tools. The structural features of parallel manipulators are dynamic, a characteristic that is particularly significant when these manipulators are used in high-speed machine tools. However, normal kinematic control method cannot satisfy the requirements of the control system. Many researchers use model-based dynamic control methods, such as the dynamic feedforward control method. However, these methods are rarely used in hybrid machine tools because of the complex dynamic model of the parallel manipulator. In order to study the dynamic control method of parallel manipulators, the dynamic feedforward control method is used in the dynamic control system of a 3-PSP (prismatic-spherical-prismatic) 3-DOF spatial parallel manipulator used as a spindle head in a high-speed hybrid machine tool. Using kinematic analysis as basis and the Newton-Euler method, we derive the dynamic model of the parallel manipulator. Furthermore, a model-based dynamic feedforward control system consisting of both kinematic control and dynamic control subsystems is established. The dynamic control subsystem consists of two modules. One is used to eliminate the influence of the dynamic characteristics of high-speed movement, and the other is used to eliminate the dynamic disturbances in the milling process. Finally, the simulation model of the dynamic feedforward control system of the 3-PSP parallel manipulator is constructed in Matlab/Simulink. The simulations of the control system eliminating the influence of the dynamic characteristics and dynamic disturbances are conducted. A comparative study between the simulations and the normal kinematic control method is also presented.The simulations prove that the dynamic feedforward control method effectively eliminates the influence of the dynamic disturbances and dynamic characteristics of the parallel manipulator on high-speed machine tools, and significantly improves the trajectory accuracy. This is the first attempt to introduce the dynamic feedfordward control method into the 3-PSP spatial parallel manipulator whose dynamic model is complex and provides a study basis for the real-time dynamic control of the high-speed hybrid machine tools.

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.