Aerial manipulation extends robotic operations to previously inaccessible aerial environments. Unlike arm-equipped aerial systems, tiltable-multirotors can directly generate six-degree-of-freedom wrenches through their flight bases, enabling both efficient movement and omnidirectional operation by tilting the thrust direction. This work presents a design analysis and a wrench-based control framework for tiltable-multirotors in aerial manipulation. We show that a four-rotor tiltable configuration provides a balance between interference-free propeller sizing and hovering efficiency across different attitudes, and its null-space redundancy is crucial for traversing singular configurations under physical constraints. We further show that an upward end-effector placement yields a favorable trade-off between geometric clearance and available wrench. To address disturbances, we propose a dual strategy consisting of a modified integral term for model error and an acceleration-based estimator for external wrenches. Building on these insights, we develop an effector-centric nonlinear model predictive control (NMPC) framework that integrates design choices, singularity handling, and disturbance compensation into a unified formulation. The proposed framework runs fully onboard at 100 Hz on a custom-built tiltable-quadrotor. Real-world experiments, including a 90-deg step cartwheel rotation, whiteboard pushing, and continuous 360-deg valve turning, demonstrate the feasibility of wrench-based omnidirectional manipulation with singularity traversal on a one-DoF-per-arm tiltable-quadrotor.

Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high-dimensional, constraint-rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization. First, we exploit the dual nature of these robots - the root link as a rigid body for guidance and the articulated joints for flexibility - to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point-cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real-world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot to generate continuous, collision-free, and dynamically feasible trajectories directly from raw point-cloud inputs, without relying on handcrafted obstacle models.

In nature, birds perch to rest and to survey their predators and prey. In human-managed contexts, perching also facilitates interaction with humans such as falconry. Recently, researchers have developed perching-capable aerial robots as a way to save energy, and deformable structures demonstrate significant advantages in efficiency of perching and compactness of configuration. However, ensuring flight stability remains challenging for deformable aerial robots due to the difficulty of controlling flexible arms. Furthermore, perching for human interaction requires high compliance along with safety. Thus, this study aims to develop a deformable aerial robot capable of perching on humans with high flexibility and grasping ability. To overcome the challenges of stability of both flight and perching, we propose a hybrid morphing structure that combines a unilateral flexible arm and pneumatic inflatable actuators. This design allows the robot's arms to remain rigid during flight and soft while perching for more effective grasping. We also develop a pneumatic control system that improves pressure regulation while integrating safe and compliant contact and adjustable grasping forces, enhancing interaction capabilities and reducing energy consumption. Besides, we focus on the structural characteristics of the unilateral flexible arm and identify sufficient conditions under which standard quadrotor modeling and control remain effective in terms of flight stability. Finally, the developed prototype demonstrates the feasibility of compliant perching maneuvers on humans, as well as the robust recovery even after arm deformation caused by thrust reductions during flight. To the best of our knowledge, this work is the first to achieve an aerial robot capable of perching on humans for interaction.

Tilt-rotor aerial robots enable omnidirectional maneuvering through thrust vectoring, but introduce significant control challenges due to the strong coupling between joint and rotor dynamics. While model-based controllers can achieve high motion accuracy under nominal conditions, their robustness and responsiveness often degrade in the presence of disturbances and modeling uncertainties. This work investigates reinforcement learning for omnidirectional aerial motion control on over-actuated tiltable quadrotors that prioritizes robustness and agility. We present a learning-based control framework that enables efficient acquisition of coordinated rotor-joint behaviors for reaching target poses in the SE(3) space. To achieve reliable sim-to-real transfer while preserving motion accuracy, we integrate system identification with minimal and physically consistent domain randomization. Compared with a state-of-the-art NMPC controller, the proposed method achieves comparable six-degree-of-freedom pose tracking accuracy, while demonstrating superior robustness and generalization across diverse tasks, enabling zero-shot deployment on real hardware.

Material-extrusion additive manufacturing (MEX-AM) involves rapid and microscopic physical phenomena, such as melting, ejection, and solidification of thermoplastic polymers that occur at the nozzle tip. These processes are highly sensitive to various process parameters, such as nozzle temperature and scan speed, rendering parameter exploration costly and time-consuming. To expedite the identification of optimal processing conditions, this study focuses on tensile strength as the target property and introduces neck width, derived from sidewall asperities, as an in situ intermediate feature (IFV). Neck width can be measured non-destructively and demonstrates a strong correlation with tensile strength. A Bayesian optimization with intermediate features (BOIF) approach is proposed and implemented for higher-throughput parameter exploration through real-time monitoring of neck width during modeling. By optimizing nozzle temperature, this method achieves a throughput approximately 400 times greater than that of exhaustive tensile testing. When the parameter space is expanded to include scan speed, throughput increases by up to 800 times. These results highlight the effectiveness of the BOIF framework in reducing both the experimental cost and development time in MEX-AM process optimization. Moreover, the framework suggests that incorporating additional IFVs can enhance predictive accuracy, and extending the optimization to higher-dimensional parameter spaces can further amplify the efficiency gains.

Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal control framework with deep learning paradigms opens up the possibility to accurately track control tasks without the need for complex analytical models. However, the system dynamics are often nuanced and the neural model lacks the potential to understand physical properties such as inertia and conservation of energy. In this work, we propose a novel energy-based regularization loss function which is applied to the training of a neural model that learns the residual dynamics of an omnidirectional aerial robot. Our energy-based regularization encourages the neural network to cause control corrections that stabilize the energy of the system. The residual dynamics are integrated into the MPC framework and improve the positional mean absolute error (MAE) over three real-world experiments by 23

Aerial interaction requires aerial robots capable of independent attitude control, leading to the rising popularity of omnidirectional tiltable multirotors (tilt-multirotors). Unlike standard multirotors that rely on differential flatness to generate high-quality trajectories, tilt-multirotors possess servo-integrated nonlinear dynamics. Consequently, their trajectory optimization becomes a challenging nonconvex problem, generally suffering from high initialization sensitivity and computational costs. To address these challenges, we propose the penalized trust-region sequential convex programming with line search (PTR-SCP-LS) framework. Specifically, we introduce a line search update strategy based on a merit function to mitigate sensitivity to penalty weights. Furthermore, the framework allows us to flexibly exploit the underlying convexity of specific tasks to reduce computational burden and enhance tractability. By recognizing that standard multirotors are special cases of tilt-multirotors, we subsume both under the unified framework of generalized multirotors. Extensive simulations and experiments evaluate the framework’s performance and generality. In a filming task with special Euclidean group SE(3) constraints using a tilt-quadrotor, with the servo-integrated nonlinear model, the line search strategy improves practical convergence robustness in the tested cases and achieves 2x faster solving times compared to nonconvex baselines with comparable solution accuracy. The proposed framework lays the foundation of the trajectory optimization for generalized multirotors, paving the way for versatile interaction tasks.

Deformable aerial robots with articulated structures have attracted increasing attention for their ability to perform complex tasks through in-flight morphological adaptation. However, most existing implementations rely on mechanical actuators, which increase total weight and vulnerability to external impacts. To address these limitations, we propose a deformable quadrotor platform actuated by antagonistic McKibben Pneumatic Actuators (MPAs), which offer lightweight, flexible, and robust actuation. We develop a dynamic model of the quadrotor incorporating joint angles actuated by MPAs, and characterize the relationship between internal pneumatic pressure and joint angle. Based on this model, we construct a prototype platform and evaluate its performance through a series of experiments. We first identify the optimal actuator length by analyzing joint deformation under varying pressure conditions. Next, we demonstrate stable flight during in-air morphing transitions, such as X-type, T-type, and H-type configurations, with position and attitude errors remaining within acceptable ranges. The results confirm that the proposed system enables stable flight using soft pneumatic actuation and paves the way for future aerial manipulation and morphing applications.

Robots are increasingly being used to replace humans in performing dangerous tasks, and aerial robots are particularly popular for work at heights. Both underactuated and fully actuated multirotors have mainly been used, but the range of tasks they can perform is limited due to their low degree of operational freedom. Articulated aerial robots are attracting attention as one solution to this problem. Due to the complexity and numerous disturbances involved in high-altitude work, teleoperation by humans is still necessary, and research is ongoing. Most of these studies focus on conventional multi-rotors, and it is difficult to intuitively control articulated aerial robots. Therefore, in this study, we propose a new teleoperation framework that allows operators to intuitively control all degrees of freedom of an articulated aerial robot simultaneously. The proposed framework consists of a floating-based device that acquires operating inputs by utilizing the freedom of movement of both human hands, and a system that generates commands to the robot from those inputs. The effectiveness of the proposed framework was verified through operating experiments using a real robot and wall cleaning task.

Multirotor aerial robots excel at maneuvering in three-dimensional space, and recent advances enable nimble navigation in cluttered and confined environments, especially for small airframes. By contrast, platforms built for high-altitude work tend to be larger to deliver high thrust for stable physical interaction with the environment. However, these conflicting design requirements create a long-standing trade-off between nimble navigation and robust aerial manipulation. Here, we present LEGION units, which are reconfigurable modular aerial robots capable of in-flight self-assembly for cooperative manipulation, drawing inspiration from the self-organized collectives formed by ants. Each unit retains nimble maneuverability while joint-equipped docking interfaces at both ends enable end-to-end self-assembly into a flying manipulator. We show that multiple units autonomously dock in flight; once latched, they maintain a zero-clearance interlock by controlling the contact force and torque, enabling reliable aggregation and articulated motion even outdoors. We further show that self-reconfigurability enables morphological switching between nimble individual flight and collective articulated manipulation, while realizing core in-flight manipulation primitives including pushing, pulling, rotating, grasping, and carrying. LEGION's self-organization enables aerial robots, especially in swarms, to shift from passive observers to active participants in their environment, broadening the scope of aerial physical interaction.

Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However, they still face substantial challenges in contact-rich aerial manipulation tasks such as surface sliding, which requires both disturbance robustness and compliance to uncertain surface geometry. Force-control strategies such as impedance and admittance control are commonly employed to address these requirements. Although impedance control can provide disturbance-resistant interaction and admittance control can offer compliant adaptation, their opposite force–motion causalities prevent their simultaneous implementation when applied through the same actuation source, such as the rotor thrusts used by conventional aerial robots. To overcome this limitation, we propose a hybrid impedance–admittance control strategy for a multi-link aerial robot. The articulated morphology enables a functional separation of force and motion regulation across joint and rotor actuation sources. In this framework, admittance behavior is generated through joint angle regulation to enhance adaptive interaction, while impedance behavior is achieved by modulating rotor thrust to regulate the sliding motion. This structural coordination allows the robot to leverage the complementary strengths of both control paradigms. As a result, the multi-link aerial robot achieves resilient and adaptive surface sliding. Experimental results demonstrate robust and compliant sliding performance on unknown surfaces.

In recent years, multimodal locomotion capabilities have enabled robots to maneuver in both terrestrial and aerial domains. However, most of these robots are designed only for locomotion, and few possess the manipulation capabilities required for practical tasks. By adding a manipulator, ground robots can perform manipulation, and some drones with robotic arms have demonstrated aerial manipulation. Nonetheless, such multirotors cannot be directly used for manipulation on the ground, and this configuration itself is unsuitable for air-ground hybrid locomotion. This is because their thruster-centralized structure makes it difficult to achieve both sufficient degrees of freedom (DoF) for manipulation and stable motion with contact and transformation. Therefore, in this work, we develop a new multilink multirotor with thrusters on each link and capable of contact with the environments. This robot can perform terrestrial rolling locomotion, aerial flight locomotion, and manipulation in multiple environments using joint actuation. First, we introduce a minimal configuration design of the proposed robot. We also describe a kinematic model and propose a design for each component based on this model. Second, we propose a real-time control method based on nonlinear optimization that considers contact and joint motion, which can be applied to various multirotors. Third, we propose motion strategies that include contact constraints specific to air-ground hybrid multilink multirotors, and analyze the limitations of manipulation capabilities based on multi-contact model. Finally, we demonstrate a variety of motions in both domains using the implemented prototype. To the best of our knowledge, this is the first demonstration of air-ground hybrid locomotion and manipulation by a multilink multirotor.

Robotic fish have attracted growing attention in recent years owing to their biomimetic design and potential applications in environmental monitoring and biological surveys. Among robotic fish employing the Body–Caudal Fin (BCF) locomotion pattern, motor-driven actuation is widely adopted. Some approaches utilize multiple servo motors to achieve precise body curvature control, while others employ a brushless motor to drive the tail via wire or rod, enabling higher oscillation and swimming speeds. However, the former approaches typically result in limited swimming speed, whereas the latter suffer from poor maneuverability, with few capable of smooth turning. To address this trade-off, we develop a wire-driven robotic fish equipped with a 2-degree-of-freedom (DoF) crank–slider mechanism that decouples propulsion from steering, enabling both high swimming speed and agile maneuvering. In this paper, we first present the design of the robotic fish, including the elastic skeleton, waterproof structure, and the actuation mechanism that realizes the decoupling. We then establish the actuation modeling and body dynamics to analyze the locomotion behavior. Furthermore, we propose a combined feedforward–feedback control strategy to achieve independent regulation of propulsion and steering. Finally, we validate the feasibility of the design, modeling, and control through a series of prototype experiments, demonstrating swimming, turning, and directional control.

Utilizing a servo to tilt each rotor transforms quadrotors from underactuated to overactuated systems, allowing for independent control of both attitude and position, which provides advantages for aerial manipulation. However, this enhancement also introduces model nonlinearity, sluggish servo response, and limited operational range into the system, posing challenges to dynamic control. In this study, we propose a control approach for tiltable-quadrotors based on nonlinear model predictive control (NMPC). Unlike conventional cascade methods, our approach preserves the full dynamics without simplification. It directly uses rotor thrust and servo angle as control inputs, where their limited working ranges are considered input constraints. Notably, we incorporate a first-order servo model within the NMPC framework. Simulation reveals that integrating the servo dynamics is not only an enhancement to control performance but also a critical factor for optimization convergence. To evaluate the effectiveness of our approach, we fabricate a tiltable-quadrotor and deploy the algorithm onboard at 100Hz. Extensive real-world experiments demonstrate rapid, robust, and smooth pose-tracking performance.

Delivery by aerial robots is an emerging topic in many scenarios, such as logistics, construction industry, and disaster response. Compared to the standard styles that deploy cage or sling, grasping style by gripper can handle objects in various shapes. A multi-limbed structure with distributed vectorable rotors called SPIDAR shows a higher potential to grasp large object in a three-dimensional manner. Therefore, in this paper, we focus on the advanced usage of the vectored thrust forces to achieve aerial grasping by this robot. First, a vectored thrust control to avoid the aerointerference on the underwind segments (e.g., grasped object) during flight is proposed. Then, an optimization-based planning method that utilizes redundant vectored thrust forces for firm grasping is developed. Finally, we demonstrate the feasibility of the proposed flight control and grasp planning by performing challenging grasping and transporting motion with a spherical object of which the diameter is 0.6 m. To the best of our knowledge, this work is the first to achieve multi-finger-like grasping to carry a large object in midair.

Birds in nature perform perching not only for rest but also for interaction with human such as the relationship with falconers. Recently, researchers achieve perching-capable aerial robots as a way to save energy, and deformable structure demonstrate significant advantages in efficiency of perching and compactness of configuration. However, ensuring flight stability remains challenging for deformable aerial robots due to the difficulty of controlling flexible arms. Furthermore, perching for human interaction requires high compliance along with safety. Thus, this study aims to develop a deformable aerial robot capable of perching on humans with high flexibility and grasping ability. To overcome the challenges of stability of both flight and perching, we propose a hybrid morphing structure that combines a unilateral flexible arm and a pneumatic inflatable actuators. This design allows the robot's arms to remain rigid during flight and soft while perching for more effective grasping. We also develop a pneumatic control system that optimizes pressure regulation while integrating shock absorption and adjustable grasping forces, enhancing interaction capabilities and energy efficiency. Besides, we focus on the structural characteristics of the unilateral flexible arm and identify sufficient conditions under which standard quadrotor modeling and control remain effective in terms of flight stability. Finally, the developed prototype demonstrates the feasibility of compliant perching maneuvers on humans, as well as the robust recovery even after arm deformation caused by thrust reductions during flight. To the best of our knowledge, this work is the first to achieve an aerial robot capable of perching on humans for interaction.

Aerial robots, especially multirotor type, have been utilized in various scenarios such as inspection, surveillance, and logistics. The most critical issue for multirotor type is the limited flight time due to the large power consumption to hover against gravity. Inspired by nature, various research areas focus on the perching and grasping ability by deploying a gripper on the multirotor to grasp arboreal environments to save energy; however, most of the mechanical design for gripper restricts the approach path, significantly limiting the performance of perching and grasping. In addition, it is also challenging to design a light gripper that also offers sufficiently large grip force to hang itself. Therefore, in this work, we develop a single-actuator hand for aerial robot that enables adaptive grasping of various objects, and thus can perch from various approach directions. First, we present the design of the lightweight three-fingered hand with a pair of special two-dimensional differential plates that enables adaptive grasping with a single actuator. In addition, we develop a unique control method for the over-actuated aerial robot equipped with this hand to perform both adaptive pendulum-like perching and detachment. Finally, we demonstrate the feasibility of the prototype hand via load bearing and object grasping experiments, along with in-flight perching experiments.