Search Results (1,436)

Motion and path planning are fundamental to intelligent robotic systems, enabling navigation. The objective is to generate collision-free trajectories in obstacle-rich configuration spaces (C-spaces) while meeting performance constraints. In environments with narrow passages planning becomes especially difficult, as feasible regions have low measure and are rarely reached by random sampling. Classical sampling-based planners are probabilistically complete but inefficient in such regions. Learning-based planners like MPNet offer fast inference but often produce infeasible paths in cluttered areas, requiring expensive postprocessing. To address this trade-off, we propose a hybrid framework that combines improved sampling, structural abstraction, and neural prediction. A modified bridge-test sampler applies directional perturbations and corridor checks to generate reliable narrow passage samples. These are clustered into a sparse set of representative bridge points, which serve as nodes in a global graph. At query time, a greedy heuristic search explores this graph, using a neural local segment generator to connect nodes. We validate the approach on 2D maze maps, 3D voxel environments, and a 12-DOF manipulator performing a plugging task inside a simulated nuclear steam generator. Across all tasks, our method significantly outperforms classical and learning-based baselines in terms of success rate and planning time in narrow-passage-dominated scenarios. The inclusion of the repair module, under relaxed assumptions, also allows the framework to retain a generalized form of probabilistic completeness. Full article

Quadruped robots possess strong adaptability to rugged terrain, soft ground, and multi-obstacle environments, offering broad application prospects in extraterrestrial planetary exploration. However, large diurnal temperature variations on extraterrestrial bodies exacerbate joint friction nonlinearity, degrading motion control accuracy and stability. To address this, a quadruped robot prototype with hybrid serial–parallel legs is designed for lunar exploration, and an 18-DOF dynamic model is derived using d’Alembert’s principle. Based on the PPO (Proximal Policy Optimization) reinforcement learning algorithm, joint friction parameters are identified using joint velocity and foot–ground contact force. By introducing friction compensation and contact force, an accurate dynamics-based feedback linearization control model is constructed, and a motion impedance control law is designed. Finally, joint friction parameters are identified and validated through both virtual and experimental prototypes, and the proposed control method is tested on flat and sloped terrain. Results show that the method can precisely regulate contact force and foot position, keeping RMSE (Root Mean Square Error) of position within 21.04 mm while preventing slipping and false contact. Full article

Over the last few years, deep neural networks have achieved outstanding results in computer vision, and have been widely integrated into mobile robot obstacle avoidance systems, where perception-driven classification supports navigation decisions. Most existing approaches rely on either color images (RGB) or depth images (D) as the primary source of information, which limits their ability to jointly exploit appearance and geometric cues. This paper proposes a deep learning-based classification approach that simultaneously exploits RGB and depth information for mobile robot obstacle avoidance. The method adopts an early-stage fusion strategy in which RGB images are first converted into the CMYK color space, after which the K (black) channel is replaced by a normalized depth map to form a four-channel CMYD representation. This representation preserves chromatic information while embedding geometric structure in an intensity-consistent channel and is used as input to a convolutional neural network (CNN). The proposed method is evaluated using locally acquired data under different training options and hyperparameter settings. Experimental results show that, when using the baseline CNN architecture, the proposed fusion strategy achieves an overall classification accuracy of 93.3%, outperforming depth-only inputs (86.5%) and RGB-only images (92.9%). When the refined CNN architecture is employed, classification accuracy is further improved across all tested input representations, reaching approximately 93.9% for RGB images, 91.0% for depth-only inputs, 94.6% for the CMYK color space, and 96.2% for the proposed CMYD fusion. These results demonstrate that combining appearance and depth information through CMYD fusion is beneficial regardless of the network variant, while the refined CNN architecture further enhances the effectiveness of the fused representation for robust obstacle avoidance. Full article

Technological innovation plays a crucial role in improving educational quality worldwide. In Ethiopia, however, literacy skills face significant obstacles, worsening the problem of learning poverty. This study aimed to analyze the effects of integrated AI educational tools on students’ literacy development. It also explored how learners perceived the use of these tools in reading and writing instruction. A quasi-experimental single-group time series design, combining both quantitative and qualitative approaches, was used. A total of 46 students from the Information Technology department at Injibara University were selected through a comprehensive census sampling method. For a period of three months, participants received reading and writing lessons supported by AI tools (NoRedInk, Rewordifyv2.1.0, and LanguageTool 9.5.0) to assess their impact on literacy skills. Data collection included pre- and post-tests, focus group discussions, and reflective journals. Quantitative data were analyzed with ANOVA, and qualitative data underwent thematic analysis using thematic techniques. Results revealed that the integration of AI educational tools significantly enhanced students’ literacy skills, including grammar, vocabulary, comprehension, content organization, and writing style. Students also expressed positive perceptions of using these tools in their reading and writing lessons. Therefore, this study encourages scholars, educators, and learners to adopt integrated AI educational tools to improve literacy development. Full article

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation. Full article

As in schools worldwide, climate change (CC) is addressed in curricula and environmental programs in Portugal. Grounded in Bandura’s human agency theory, effective CC mitigation requires the capacity to intentionally initiate, sustain, and reflect on behaviors to reduce greenhouse gas emissions, i.e., climate agency. This study aimed to map school’s role (environmental initiatives and CC teaching) in developing students’ climate agency and its determinants. Participants included 42 school representatives and 24 teachers from various subjects. Data sets, collected through online surveys, semi-structured interviews, and a focus group, were analyzed using content analysis. School representatives emphasized school initiatives requiring significant levels of student engagement (e.g., cleanups) but with limited participation. Most teachers reported employing transmissive teaching approaches, complemented by audio–visual resources and classroom discussions. Interviewees identified facilitators (e.g., family pro-environmental behaviors and municipal support), but mostly obstacles (e.g., limited instruction time and surface approach to learning) that contributed to a perceived minimal impact of CC education on their students. Overall, the data suggest that current environmental programs and CC teaching are not consistently developing students’ climate agency. The findings highlight the need to rethink formal and informal approaches to promote high-quality CC education and student agency in addressing the climate crisis. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

It has been widely demonstrated that primary school students face cognitive obstacles in scientific argumentation and often employ ineffective strategies. Scaffolding can effectively guide the scientific argumentation process, enhance its logical rigor, and facilitate students’ reflective thinking during argumentation. The purpose of this study is to examine the effects of question prompts and worked examples on primary school students’ scientific argumentation skills, alongside their scientific achievement, learning motivation and cognitive load. Using a quasiexperimental design, this study involved 68 fourth-grade students and compared the effects of two types of scaffolding: question prompts and worked examples. The results show that both scaffolding strategies exerted positive effects on students’ scientific argumentation skills, scientific achievement and learning motivation. More importantly, the worked examples were significantly more effective than the question prompts in enhancing scientific argumentation skills, particularly in terms of evidence integration and logical reasoning, and they provided greater assistance to students with low levels of prior knowledge. Finally, the worked examples group exhibited significantly lower extraneous cognitive loads than the question prompts group did. This study provides empirical evidence for optimizing the scaffolding design of primary scientific argumentation teaching, confirming that worked examples offer more efficient and adaptive support for novice learners in primary schools in a short time period. From a long-term developmental perspective, it is necessary to gradually fade the support of worked examples and transition to question prompts in scientific argumentation instruction, so as to prompt students to invest more cognitive effort and foster their independent argumentation and critical thinking abilities. These findings have important implications for advancing science curriculum reform and designing targeted instructional interventions. Full article

Tight-formation control is a key technology for unmanned surface vehicle (USV) swarms in harbor navigation, cooperative berthing, and operations in hazardous environments, yet achieving reliable obstacle avoidance while maintaining formation stability remains highly challenging. Although multi-agent reinforcement learning has shown strong potential in cooperative systems, parallel policy structures in many existing methods still struggle to achieve synchronized coordination in tight formations, leading to behavioral inconsistencies and unstable formation keeping. To address these challenges, an action-aware multi-agent soft actor–critic (AAMASAC) algorithm is proposed that introduces a hierarchical, action-aware decision mechanism. Within each time step, upper-layer actions are propagated as prior signals to lower-layer policies, establishing an ordered, intent-aligned decision flow that mitigates temporal inconsistency and enhances coordination efficiency. The architecture explicitly encodes inter-layer dependencies via a decision priority hierarchy and real-time behavioral information channels, enabling more accurate credit assignment and more stable value estimation and policy optimization. Across three representative validation scenarios, the AAMASAC algorithm significantly outperforms baseline methods in average reward, path-tracking accuracy, formation stability, and obstacle-avoidance performance. These results indicate that introducing a hierarchical model and action awareness effectively improves control accuracy and coordination in a USV swarm. Full article

Spacecraft safe proximity, as a critical component of on-orbit servicing missions, primarily encounters the following two challenges: the partial observability of the environment surrounding the service spacecraft and the necessity to evade uncertain obstacles. A safe reinforcement learning algorithm based on a graph neural network is proposed to address the constrained Markov decision problem in partially observable scenarios for spacecraft safe proximity missions. A graph neural network mechanism is introduced to solve the problem of dynamic variations in the quantity and location of obstacles in the observation area of the service spacecraft. The graph attention network is used to facilitate the extraction of feature information from the graph structure, which is then utilized as input for the subsequent reinforcement learning algorithm. The Soft Actor–Critic–Lagrangian algorithm is adopted to deal with the problems of tuning reward function parameters and balancing safety and optimality. By introducing Lagrange multipliers, the constrained optimization problem is transformed into an unconstrained optimization problem. In order to verify the effectiveness of the algorithm proposed in this paper, a spacecraft safe proximity environment model with dynamic obstacles is constructed, and the GAT-SACL algorithm proposed in this paper is validated by the Monte Carlo shooting method. The results show that the GAT-SACL algorithm possess excellent exploratory characteristics and delivers significant advantages in balancing optimality and safety. Full article

In recent years, autonomous underwater vehicles (AUVs) have been increasingly employed for target surveillance and tracking. However, the limited performance and information-processing capability of a single AUV make it difficult to achieve high-precision tracking in practice. To address these challenges, this paper proposes an online-to-offline multi-agent reinforcement learning (MARL) framework that employs offline training on historical data to obtain the expert policy. Then, the optimal policy is generated by online fine-tuning technology, which enhances the training efficiency of reinforcement learning in new scenarios. To expand the surveillance range of AUV swarms, a distributed cooperative strategy based on area information entropy (AIE) is introduced. To reduce energy consumption in complex marine environments containing obstacles and vortices, ocean current and energy consumption models are introduced, together with an energy-efficiency optimization strategy. Furthermore, a long short-term memory (LSTM) network is integrated into the offline-to-online MARL framework to predict time-varying environmental states, thereby improving tracking accuracy and energy efficiency. Experimental results show that the proposed scheme is superior to the baseline schemes in terms of energy consumption, task success rate, and distance between AUVs. In addition, various performance indicators of the extended AUV swarm are also superior to the baseline schemes, demonstrating that the proposed scheme has excellent performance and scalability. Full article

A two-layer control framework for unmanned underwater vehicle (UUV) navigation is proposed, combining a lower-layer active disturbance rejection controller (ADRC) with an upper-layer safe reinforcement learning (RL) policy for obstacle-avoidance navigation. The lower layer, utilizing ADRC, ensures high tracking accuracy and effective disturbance rejection, while the upper layer integrates the twin delayed deep deterministic policy gradient (TD3) algorithm, combined with a control barrier function (CBF)-based quadratic programming (QP) safety filter and safety-inspired reward shaping (SR). The method is evaluated in two simulation studies: (i) velocity and attitude control to assess tracking and disturbance rejection, and (ii) obstacle-avoidance navigation to assess learning efficiency, trajectory smoothness, and safety-related metrics. Simulation results show that ADRC achieves faster tracking and stronger disturbance rejection than a conventional proportional–integral–derivative (PID) controller. Moreover, the proposed TD3 + QP + SR scheme exhibits faster learning, smoother trajectories, and improved safety performance compared with RL baselines. These results indicate that the proposed framework enables efficient and safe UUV navigation in simulation scenarios with obstacles and disturbances. Full article

Background/Objectives: Obesity remains a major global health burden, with metabolic–bariatric surgery being the most efficient long-term treatment strategy. However, both variability in perioperative care and postoperative complications persist. To address these challenges, the EUropean PErioperative MEdical Networking (EUPEMEN) protocol for bariatric surgery was developed to standardize care and enhance perioperative outcomes across European healthcare settings. Methods: The protocol was formulated through close collaboration among experts from multiple disciplines, involving surgeons, anesthetists, nurses, and nutritionists. Its development included a literature review, expert consensus, and the creation of structured perioperative guidelines covering the preoperative, intraoperative, and postoperative phases. Focus areas include patient education, nutritional optimization, early mobilization, opioid-sparing analgesia, and minimally invasive surgical techniques, supported by educational materials and manuals. Technical activities included the development of detailed multimodal rehabilitation manuals translated into five languages, the creation of an open-access online learning platform, training of future educators through a “train the trainer” approach, organization of multiplier promotional events, international collaboration meetings to refine the protocol, and revision and standardization of existing perioperative care guidelines to ensure evidence-based, unified practices across Europe. Results: Implementation of the EUPEMEN protocol aims to reduce postoperative complications, enhance recovery, and decrease hospitalization time. Standardized rehabilitation pathways and access to free educational platforms promote consistent care delivery across diverse healthcare environments. Key strategies include early oral intake, limited use of invasive devices, and comprehensive patient preparation. Conclusions: The EUPEMEN protocol introduces an evidence-based, multidisciplinary framework for optimizing perioperative management in bariatric surgery. While variability in resources and adherence may present potential obstacles, its application holds significant promise for improving perioperative outcomes. Future studies are necessary to assess its long-term impact and adaptability in different healthcare settings. Full article

The rapid expansion of low-altitude unmanned aerial vehicles demands robust obstacle detection and avoidance systems capable of operating under diverse environmental conditions. This paper proposes a multimodal fusion attention network that integrates visual imagery and Light Detection and Ranging (LiDAR) point cloud data for real-time obstacle perception. The architecture incorporates a bidirectional cross-modal attention mechanism that learns dynamic correspondences between heterogeneous sensor modalities, enabling adaptive feature integration based on contextual reliability. An adaptive weighting component automatically modulates modal contributions according to estimated sensor confidence under varying environmental conditions. The network further employs gated fusion units and multi-scale feature pyramids to ensure comprehensive obstacle representation across different distances. A hierarchical avoidance decision framework translates detection outputs into executable control commands through threat assessment and graduated response strategies. Experimental evaluation on both public benchmarks and a purpose-collected low-altitude obstacle dataset demonstrates that the proposed method achieves 84.9% mean Average Precision (mAP) while maintaining 47.3 frames per second (FPS) on Graphics Processing Unit (GPU) hardware and 23.6 FPS on embedded platforms. Ablation studies confirm the contribution of each architectural component, with cross-modal attention providing the most substantial performance improvement. Full article

As an important ecological obstacle prone to climatic changes, the Tibetan Plateau has been transformed by retreating glaciers, degrading permafrost, and deteriorating grasslands. Recent ecological remote sensing evaluations typically use medium-resolution and single-source optical imagery, highlight natural factors while ignoring human impacts, and encounter difficulties with time-focused interpretability and continuity within complex terrains. This research proposes a theory combining large language models with satellite embedding to holistically examine the ecology of the Tibetan Plateau between 2000 and 2024. We created an ecological satellite embedding (ESE) model applying self-supervised learning to integrate 12 ecological variables into combined space and time representations as of 2024, according to the Prithvi-Earth Observation (Prithvi-EO) foundational model involving low-rank adaptation (LoRA). GeoChat reasoning was applied to turn the embedded variables into a comprehensive representation feature (CRF). Field research demonstrated strong accuracy for the fraction of absorbed photosynthetically active radiation (FAPAR, R² = 0.9923) and aboveground biomass (AGB, R² = 0.8690). Space and temporal analyses demonstrated a general ecology-dependent enhancement accompanied by significant space-based clustering (Moran’s I = 0.50–0.80), hotspots in humid southeastern areas, major upward trends in vegetation indices and productivity metrics (p < 0.05), and higher shifts in transition regions. Despite the marginal degradation risk, the grassland carrying capacity has expanded extensively in the main farming regions. The comprehensible CRF schema identified three management areas: potential risk, enhancement potential, and stable conservation management. This transferable modular approach connects expert reasoning with data-driven modeling, presenting adaptable methods for assessing ecosystems in high-altitude, data-sparse environments, and practical ways to promote ecological management. Full article