A fusion-based positioning and adaptive guidance method for precise USV docking

Literacy is a fundamental prerequisite for developing students’ academic competencies. However, the 2022 PISA survey revealed that Indonesian students’ reading literacy remains low, especially in remote areas such as SDN 4 Sembiran, which faces limited internet access and challenging socio-economic conditions. This community service aims to improve basic literacy skills through an integrated learning approach combining contextual teaching and problem-based learning, based on students’ ability grouping. The method involved eight literacy training sessions supported by participatory observation and formative evaluation. Results showed significant progress in students’ ability to recognize letters, write, read syllables, words, simple sentences, and comprehend texts. More advanced students were able to read short passages, answer text-based questions, and compose simple paragraphs. The main challenges were pronunciation of consonant clusters and mastery of silent letters, which were gradually overcome with consistent guidance and adaptive methods. This program highlights the importance of ability-based grouping, integration of contextually relevant teaching methods, and active literacy facilitation. Approaches linking learning materials to real-life contexts and problem-solving effectively enhanced students’ motivation and literacy skills in remote areas. Structured and sustainable learning strategies are key to successfully improving basic literacy across varying student abilities.

This paper proposed a reinforcement learning-based adaptive guidance method for a class of spiral-diving manoeuver guidance problems of reentry vehicles subject to unknown disturbances. First, the desired proportional navigation guidance law is designed for the vehicle based on the initial conditions, terminal constraints and the curve involute principle. Then, the first-order multivariable nonlinear guidance command tracking model considering unknown disturbances is established. And the controller design problem caused by the coupling of control variables is overcome by introducing the coordinate transformation technique. Moreover, the actor-critic networks and corresponding adaptive weight update laws are designed to cope with unknown disturbances. With the help of Lyapunov direct method, the stability of the system is proved. Subsequently, the range values of the guidance parameters are analysed. Finally, the validity as well as superiority of the proposed method are verified by numerical simulations.

In the past decade, adaptive design methods in clinical research have attracted much attention because it offers not only the principal investigators potential flexibility for identifying clinical benefit of a test treatment under investigation, but also efficiency for speeding up the development process. The FDA adaptive design draft guidance defines an adaptive design as a clinical study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study.

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

The proposed robust nonlinear disturbance observer based adaptive guidance method considers the nonlinear coupled missile dynamics and rapidly maneuvering target. It also includes unavailable information such as high-order line-of-sight rates and target acceleration as time-varying parametric uncertainties and nonparametric disturbances. An integrated guidance and control model, a robust nonlinear disturbance observer, a nonlinear missile jerk observer, and a robust nonlinear guidance law are proposed, the validity of which is demonstrated by stability analysis and simulations.

This paper introduces an innovative hierarchical framework tailored to meet the offensive and defensive needs of swarm-based unmanned surface vehicles (USVs) for domain protection and invasion tasks. The framework is structured into three layers: The top layer handles target allocation, the middle layer focuses on action strategy, and the bottom layer ensures safety. For domain protection tasks, the top layer leverages auction theory to effectively match attackers and defenders, enabling the distributed allocation of multiple defenders to respective invaders. The middle layer employs an adaptive guidance method, allowing each defender to follow a guidance law aligned with the optimal interception point. The bottom layer uses an enhanced potential field function to guarantee collision-free interception of invaders. For invasion tasks, the top layer similarly applies auction theory for the allocation of virtual waypoints. The middle layer introduces an Artificial Potential Field (APF)-based angle-adaptive attack strategy, enabling invaders to evade defender interception. The bottom layer remains consistent with the domain protection framework. This paper marks the first hybrid virtual-real experiment in multi-USV confrontation, demonstrating its reliability and effectiveness in real maritime conditions. Experiments conducted within a 5000-m radius, with vessels traveling at speeds up to 12[Formula: see text]m/s, confirmed the framework’s robustness and practical applicability in near-realistic combat scenarios.

A new docking method for mobile robot is proposed. The desired docking state (desired position and orientation) of the robot is defined by a pre-captured reference image. The scale invariant feature transform (SIFT) algorithm and a double direction best bin first (BBF) based feature matching algorithm are used to extract the visual feedback information by matching the current view of the docking station and the reference image. The robot is aligned against the reference image based on a epipole servoing strategy. A centroid tracking is used to avoid the object escaping from the field of view. A random sample consensus (RANSAC) algorithm is used to solve the affine transform between current and reference images, and a control rule at last stage is presented to realize precise docking. No more knowledge of the scene geometry or artificial marks are needed. The experiment results of a realistic indoor environment demonstrate the effectiveness of the proposed method.

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

Segmented guidance law for single moving mass controlled reentry vehicle with multiple constraints

Fuzzy logic control with adaptive methods for vehicle lateral guidance : Hessburg, Thomas Michael, Ph.D. University of California, Berkeley, 1994. 212 pp. Chair: Masayoshi Tomizuka

The entry-glide guidance strategy for hypersonic vehicles that can satisfy both terminal and path constraints is investigated in this paper. We propose a quasi-equilibrium glide adaptive guidance methodology based on the quasi-equilibrium glide condition (QEGC), which innovatively utilizes the quasi-equilibrium glide phenomenon in lifting entry. With the aid of QEGC, both range and terminal velocity can be predicted analytically with high precision. The path constraints are converted into angle of attack constraints, which has been difficult to realize by using traditional predictive guidance methods. The algorithm is independent of the standard trajectory. All the guidance commands, including the bank angle and the angle of attack, are calculated analytically in real time, which endows the algorithm with sufficient adapbility. The results of a CAV-H vehicle guidance test show that the algorithm leads the vehicle along a quasi-equilibrium glide trajectory satisfying both the terminal and path constraints and has sufficient flexibility for occasional mission changes. Furthermore, the robustness of the guidance algorithm under disturbances is validated through a Monte Carlo simulation.