This study proposes a novel approach that quantifies the local distribution of point clouds to define Dimensionality, incorporates it into the existing 3D coordinate system to construct a 4D coordinate system, and employs Hilbert Curve to efficiently improve registration speed. By analyzing the distributional characteristics of point clouds using Hellinger Distance and introducing Hilbert Curve-based nearest neighbor search, the proposed method reduces computational overhead while enhancing registration accuracy. The algorithm was evaluated on the Kitti and Stanford Bunny datasets [1], demonstrating lower performance compared to existing algorithms in the simple urban environment of the Kitti dataset but achieving outstanding registration performance in the complex structure of the Stanford Bunny dataset, even in cases involving partial overlaps and outliers. In particular, the combination of Dimensionality and Hilbert Curve-based search significantly improved registration quality and stability in complex datasets. This approach can be extended to LIO-based odometry systems or utilized in Color-based ICP applications.

The widespread adoption of depth cameras has been driven by their cost-effectiveness and depth measurement reliability across diverse applications. These sensors, however, face common challenges inherent to optical systems, particularly motion blur artifacts when deployed on moving platforms. This paper introduces a novel end-to-end approach for motion blur removal in Amplitude Modulated Continuous Wave (AMCW) depth cameras, specifically designed to handle High Dynamic Range (HDR) depth imagery. Our method leverages an optical flow network trained on Differential Correlation Sample (DCS) images to achieve proper alignment prior to phase unwrapping. We present a new real-HDR DCS dataset and develop an unsupervised training framework utilizing custom loss functions with adaptive scheduling, eliminating the need for ground truth data. While our implementation did not achieve fully successful optical flow generation for phase unwrapping, our experiments demonstrate the potential viability of optical flow-based approaches for depth image deblurring.

Although robot manipulators are widely used in advanced industrial applications, their dynamics has very high complexity and uncertainties, making exact mathematical modeling difficult and preventing high-precision tracking control. Herein, we propose a practical high-performance model-free controller for robot manipulators that attenuates the undesirable side effects of a time-delayed state-based estimation (TDE) technique in terms of the receding-horizon$\mathcal {H}_\infty$performance. By constructing tracking error dynamics with a sliding variable, the effects of TDE errors are identified and suppressed each time in terms of the fixed-horizon$\mathcal {H}_\infty$performance. All initial states are shown to converge to a certain bounded set within a precomputed finite time. The proposed approach is beneficial for sudden transient responses and the temporarily unbounded TDE errors that cannot be handled by existing TDE-based controllers. Finally, the stability of the proposed controller is proven based on the comparison Lemma, and simulations and experiments show that its tracking performance and robustness are superior to those of conventional control algorithms.

As artificial intelligence (AI) technologies have advanced, neural network-based aging estimation has been extensively studied for lithium-ion batteries. Its performance has also been steadily enhanced with sophisticated neural network designs and machine learning skills. However, utilizing well-designed AI-based aging estimators needs more research. This study shows that the same AI-based aging estimator performs differently depending on the input current signal. This article proposes a reinforcement learning (RL)-based framework with digital twin technology using an elaborate electrochemical model strategically to determine an effective input current shape that maximizes the accuracy of a given network-based estimator. As part of RL, the policy network is trained through the digital twin model to generate an input current signal. This enables a more accurate aging estimation, considering the battery's electrochemical states and the initial state of charge (SOC). By specifying a reward for RL training as the estimation error in response to the designed input data, the input current is updated over the training episodes to improve its aging estimation ability. Experimental results show that the RL-based input current signal exhibits an approximately twice more accurate aging estimation than the input signals previously used to train the given neural network-based estimator.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

Typical drones with multi rotors are generally less maneuverable due to unidirectional thrust, which may be unfavorable to agile flight in very narrow and confined spaces. This paper suggests a new bio-inspired drone that is empowered with high maneuverability in a lightweight and easy-to-carry way. The proposed flying squirrel inspired drone has controllable foldable wings to cover a wider range of flight attitudes and provide more maneuverable flight capability with stable tracking performance. The wings of a drone are fabricated with silicone membranes and sophisticatedly controlled by reinforcement learning based on human-demonstrated data. Specially, such learning based wing control serves to capture even the complex aerodynamics that are often impossible to model mathematically. It is shown through experiment that the proposed flying squirrel drone intentionally induces aerodynamic drag and hence provides the desired additional repulsive force even under saturated mechanical thrust. This work is very meaningful in demonstrating the potential of biomimicry and machine learning for realizing an animal-like agile drone.

We present a novel actor-critic algorithm for an environment with delayed feedback, which addresses the state-space explosion problem of conventional approaches. Conventional approaches use an augmented state constructed from the last observed state and actions executed since visiting the last observed state. Using the augmented state space, the correct Markov decision process for delayed environments can be constructed; however, this causes the state space to explode as the number of delayed timesteps increases, leading to slow convergence. Our proposed algorithm, called Belief-Projection-Based Q-learning (BPQL), addresses the state-space explosion problem by evaluating the values of the critic for which the input state size is equal to the original state-space size rather than that of the augmented one. We compare BPQL to traditional approaches in continuous control tasks and demonstrate that it significantly outperforms other algorithms in terms of asymptotic performance and sample efficiency. We also show that BPQL solves long-delayed environments, which conventional approaches are unable to do.

This paper proposes a new adaptive law based on sliding-mode control(SMC) control. The proposed adaptive law adjusts the switching gain near the sliding manifold to have the appropriate attractivity property. This appropriate attractivity property prevents unexpected errors caused by the excessive or insufficient adaptation of switching gain. Furthermore, this adaptive law ensures the fast adaptation speed of switching gain, and this yields the reduction of chattering. For the more practical implementation based on a model-free controller, the proposed adaptive SMC (ASMC) works together with a time-delay controller(TDC). To validate the proposed adaptive law, the results of simulations are carried out and comparisons are made with existing ASMC control schemes.

The electrochemical model is one of the important model of battery, since it represents the real dynamics in the cell. The parameters of the model have physical meanings, so estimating the parameters, especially aging parameters might be helpful to efficient cell operation. There are many parameter estimation algorithms now on. Among the algorithms, harmony search is better than other algorithms like genetic algorithm, particle swarm optimization or gradient-descent methods, since harmony search has fast process characteristic which is suitable to electrochemical model. The 5 algorithms are applied to same parameter estimating problem to compare their performance in the aspect of objective values and parameter errors. The results show that innovative global harmony search is effective to estimate the better solutions with small parameter errors.

This demonstration introduces SiCi (Smart ideas for Creative interplay) that brings a single-body robot to life by delivering a variety of contents and then fosters children's creativity and innovation in education.

The extent of glacier terminus displacement is instrumental in investigations of natural or artificial geographic changes. Its importance to earth science and engineering is reflected in the considerable efforts that have been devoted to the development of several boundary displacement analysis methods. Among the methods, the buffering-based approach compares favorably with other approaches in objectivity and robustness. However, it does not consider the relative positions of boundaries, because its buffering operation cannot determine features' relative directions. This limitation incurs inaccurate calculation results – underestimation of mean shifts and overestimation of shape variations, especially when the two compared boundaries intersect. Discrete displacement analysis (DDA), an alternative method that considers given geographic objects as a set of a finite number of points, is proposed here. In a series of tests carried out, including Jakobshavn glacier's calving front, DDA was found to correctly calculate mean shift and shape variation even in cases where the conventional buffering-based method failed. Moreover, this approach is independent of the dimension of space in which it is implemented, and thus is expected to be utilized for analysis of 3D geographic object displacement.

In this letter, we propose an optimal finite impulse response (FIR) filter with fading memory for a class of continuous-time state space models. The proposed optimal FIR filter with fading memory is linear with respect to outputs on the recent finite time horizon and it has a kernel function that is obtained from the classical least squares approach with weighting parameters by using the result on the linear quadratic tracking control. For the fast tracking ability, the less weight is put on to the older data. If the same weight is assigned to all data involved, the proposed FIR filter is shown to be reduced to the existing minimum variance unbiased FIR (MVUF) filter for a stochastic system. A numerical example is presented to illustrate the performance of the proposed optimal FIR filter with fading memory by comparing with the conventional Kalman infinite impulse response (IIR) filters and the MVUF filter.

This letter proposes robust finite impulse response (FIR) filters for linear continuous-time statespace models with bounded uncertainties. A set of all reachable current states under bounded uncertainties is determined from inputs and outputs on a recent finite time interval. If some condition is met, this set is shown to be represented in an ellipsoidal form. The derivation procedure is much simplified by utilizing the result on the optimal tracking control with an indefinite cost function. In order to minimize the maximum estimation error due to uncertainties, the center of the reachable ellipsoidal set is chosen as an estimated state. It is shown through simulation that the proposed robust FIR filter achieves a more robust performance than existing robust infinite impulse response (IIR) filters.

In this letter, we propose a fixed-lag finite impulse response (FIR) smoother for a class of continuous-time state space signal models. The proposed fixed-lag FIR smoother is linear with respect to outputs on the recent finite time interval and has a kernel function depending only on given system parameters. It is optimal in the sense that the variance of the estimation error is minimized with the unbiased constraint. The fixed-lag minimum variance FIR (MVF) smoother has a closed-form solution and can be easily extended to an FIR filter by setting the lag size to zero. A simulation example is presented to illustrate the performance of the proposed MVF smoother by comparing with a conventional fixed-lag Kalman infinite impulse response (IIR) smoother.

We propose a fixed-lag finite-impulse-response (FIR) smoother for a discrete-time state space model. The proposed FIR smoother estimates the state at the fixed-lag time using measured output samples on the recent finite time horizon so that the variance of the estimation error is minimized. The minimum variance FIR (MVFIR) smoother is unbiased and independent of any a priori information of the state on the horizon. A numerical example shows that the proposed MVFIR smoother has better performance than the fixed-lag Kalman smoother based on the infinite impulse response structure when transitory modeling uncertainties exist.

This letter proposes a robust finite impulse response (FIR) filter for a class of linear discrete-time systems with quadratic bounded uncertainties. A set of all reachable current states under uncertainties is obtained from inputs and outputs measured on a recent finite time interval, which is represented in an ellipsoidal form. To minimize the maximum estimation error due to uncertainties, the center of the ellipsoid is chosen as an estimated state and the corresponding estimation error is obtained from the major axis of the ellipsoid. It is shown by simulation that the proposed robust FIR filter has a more robust performance than both robust infinite impulse response (IIR) filters and nominal FIR filters.