Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid rotor saturation and mitigate overshoot during transition between free-flight and perching. To enable stable perching and unperching maneuvers on/from a vertical surface, a lightweight (≈ 1 kg), fully actuated tiltrotor that can hover at 90◦pitch angle is first developed. We design a perching/unperching module composed of a single servomotor and a magnet, which is then mounted on the tiltrotor. A switching controller including exclusive control modes for transitions between free-flight and perching is proposed. Lastly, we propose a simple yet effective strategy to ensure robust perching in the presence of measurement and control errors and avoid collisions with the perching site immediately after unperching. We validate the proposed framework in experiments where the tiltrotor successfully performs perching and unperching on/from a vertical surface during flight. We further show effectiveness of the proposed transition mode in the switching controller by ablation studies where large overshoot and even collision with a perching site occur. To the best of the authors’ knowledge, this work presents the first autonomous aerial unperching framework using a fully actuated tiltrotor.

As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal performances. In this paper, we present Semantic-aware Neural Radiance Fields for Reinforcement Learning (SNeRL), which jointly optimizes semantic-aware neural radiance fields (NeRF) with a convolutional encoder to learn 3D-aware neural implicit representation from multi-view images. We introduce 3D semantic and distilled feature fields in parallel to the RGB radiance fields in NeRF to learn semantic and object-centric representation for reinforcement learning. SNeRL outperforms not only previous pixel-based representations but also recent 3D-aware representations both in model-free and model-based reinforcement learning.

This study presents a new hardware design and control of a minimally actuated 5 control degrees of freedom (CDoF) quadrotor-based tiltrotor. The proposed tiltrotor possesses several characteristics distinct from those found in existing works, including: 1) minimal number of actuators for 5 CDoF, 2) large margin to generate interaction force during aerial physical interaction (APhI), and 3) no mechanical obstruction in thrust direction rotation. Thanks to these properties, the proposed tiltrotor is suitable for perching-enabled APhI since it can hover parallel to an arbitrarily oriented surface and can freely adjust its thrust direction. To fully control the 5-CDoF of the designed tiltrotor, we construct an asymptotically stabilizing controller with stability analysis. The proposed tiltrotor design and controller are validated in experiments where the first two experiments of x, y position tracking and pitch tracking show controllability of the added CDoF compared to a conventional quadrotor. Finally, the last experiment of perching and cart pushing demonstrates the proposed tiltrotor's applicability to perching-enabled APhI.

Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space. Thus, they usually rely on manually specified goal spaces. To alleviate this limitation and improve the scalability of the curriculum, we propose a novel curriculum method that automatically defines the semantic goal space which contains vital information for the curriculum process, and suggests curriculum goals over it. To define the semantic goal space, our method discretizes continuous observations via vector quantized-variational autoencoders (VQ-VAE) and restores the temporal relations between the discretized observations by a graph. Concurrently, ours suggests uncertainty and temporal distance-aware curriculum goals that converges to the final goals over the automatically composed goal space. We demonstrate that the proposed method allows efficient explorations in an uninformed environment with raw goal examples only. Also, ours outperforms the state-of-the-art curriculum RL methods on data efficiency and performance, in various goal-reaching tasks even with ego-centric visual inputs.

Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction. However, previous HRL algorithms often suffer from serious data inefficiency as environments get large. The extended components, $i.e.$, goal space and length of episodes, impose a burden on either one or both high-level and low-level policies since both levels share the total horizon of the episode. In this paper, we present a method of Decoupling Horizons Using a Graph in Hierarchical Reinforcement Learning (DHRL) which can alleviate this problem by decoupling the horizons of high-level and low-level policies and bridging the gap between the length of both horizons using a graph. DHRL provides a freely stretchable high-level action interval, which facilitates longer temporal abstraction and faster training in complex tasks. Our method outperforms state-of-the-art HRL algorithms in typical HRL environments. Moreover, DHRL achieves long and complex locomotion and manipulation tasks.

In this article, we introduce a translational force control method with disturbance observer (DOB)-based force disturbance cancellation for precise 3-D acceleration control of a multi-rotor unmanned aerial vehicle (UAV). The acceleration control of the multi-rotor requires conversion of the desired acceleration signal to the desired roll, pitch, and total thrust. However, because the attitude dynamics and the thrust dynamics are different, simple kinematic signal conversion without consideration of those difference can cause serious performance degradation in acceleration tracking. Unlike most existing translational force control techniques that are based on such simple inversion, our new method allows controlling the acceleration of the multi-rotor more precisely by considering the dynamics of the multi-rotor during the kinematic inversion. By combining the DOB with the translational force system that includes the improved conversion technique, we achieve robustness with respect to the external force disturbances that hinder the accurate acceleration control. μ-analysis is performed to ensure the robust stability of the overall closed-loop system, considering the combined effect of various possible model uncertainties. Both simulation and experiment are conducted to validate the proposed technique, which confirms the satisfactory performance to track the desired acceleration of the multi-rotor.

In this paper, we introduce a cargo transportation method with a new type of multi-rotor UAV platform known as T3-multirotor, to achieve stable and constant flight performance regardless of the type of cargo attached to the fuselage. The T3-multirotor, which consists of the `Thrust Generating Part' and the `Fuselage Part', can directly control the relative attitude between the two parts using the novel servomechanism. By utilizing the servomechanism with the proposed relative attitude control strategy, the T3-multirotor with cargo attached to the fuselage part can behave as a multi-rotor with only the moment of inertia of the thrust generating part during entire transportation. This allows the T3-multirotor to achieve the reliable performance in the event of any cargo being attached to the fuselage, achieving stable platform motion control. Detailed hardware description and dynamic analysis of T3-Multirotor is performed in this paper, and the validity of the proposed control strategy is also analyzed. The feasibility of the proposed control strategy is verified through experimental results with analysis.

This paper presents a new design of multirotor, named as `Tilting Thruster Type' (T3)-multirotor. The new platform is equipped with mechanically separated thrusters, which can take any fuselage posture within a specified range regardless of any direction of translational acceleration. A specially designed servo-linkage mechanism is employed for relative attitude control between the thruster and the fuselage. Mathematical modeling and analysis of the new platform are conducted to explore the control method of the dynamically complex system. For demonstrating the potential of the new T3-multirotor, an autonomous level flight is performed where the fuselage maintains zero roll and pitch angle during the entire flight. Both simulation and experimental results are provided with detailed analysis.

This paper presents a practical swing-angle estimation method for slung load operations of the multi-rotor unmanned aerial vehicle (UAV), which is essential to maintain the safety during the operation. In order not to rely on extra sensors for monitoring the swing angle, the proposed method in this paper offers an autonomous swing-angle estimation using only an inertial measurement unit (IMU) and a single load cell attached to the slung load. The disturbance observer (DOB) derived external force estimation is performed to estimate the swing angle. The unique structure of the proposed DOB-based disturbance force estimation technique utilizes the IMU data only. Both simulation and actual experiment are performed to validate the feasibility of the proposed algorithm.