Search Results (378)

To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article

Magnetic fields (MFs), including static (SMFs) and extremely low-frequency electromagnetic fields (ELF-EMFs), have recently emerged as potential modulators of anticancer drug responses. Evidence indicates that MFs can influence membrane transport, oxidative stress, DNA damage, apoptosis, and cell cycle regulation, thereby altering the efficacy of chemotherapeutics and targeted agents. These effects are strongly dependent on MFs’ parameters and biological context, leading to synergistic, antagonistic and no-effect outcomes. However, inconsistent exposure protocols, limited reproducibility, and scarce clinical validation remain major obstacles. This review highlights current experimental findings, proposes mechanistic links between MFs and drug action, and outlines key challenges for advancing MF-based adjuvant strategies in oncology. Full article

Robot navigation in confined spaces has gained popularity in recent years, but offline planning assumes static obstacles, which limits its application to online path-planning. Several methods have been introduced to perform an efficient robot navigation process. However, various existing methods mainly depend on pre-defined maps and struggle in a dynamic environment. Also, diminishing the moving costs and detour percentages is important for real-world scenarios of robot navigation systems. Thus, this study proposes a novel perceptron-Q learning fusion (PQLF) model for Robot Navigation to address the aforementioned difficulties. The proposed model is a combination of perceptron learning and Q-learning for enhancing the robot navigation process. The robot uses the sensors to dynamically determine the distances of nearby, intermediate, and distant obstacles during local path-planning. These details are sent to the robot’s PQLF Model-based navigation controller, which acts as an agent in a Markov Decision Process (MDP) and makes effective decisions making. Thus, it is possible to express the Dynamic Robot Navigation in a Confined Indoor Environment as an MDP. The simulation results show that the proposed work outperforms other existing methods by attaining a reduced moving cost of 1.1 and a detour percentage of 7.8%. This demonstrates the superiority of the proposed model in robot navigation systems. Full article

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color data when lidar is used, limited obstacle perception when only color imaging is used, a low field of view from a single RGB-D sensor, or the requirement for the landing spot to be prepared in advance. In this paper, a new approach is proposed where an RGB-D camera mounted on a gimbal is used. The gimbal is actively actuated to counteract the limited field of view while color images and depth information are provided by the RGB-D camera. Furthermore, a combined UAV-and-gimbal-motion strategy is proposed to counteract the low maximum range of depth perception to provide static obstacle detection and avoidance, while preserving safe operating conditions for low-altitude flight, near potential obstacles. The system is developed using a PX4 flight stack, CubeOrange flight controller, and Jetson nano onboard computer. The system was flight-tested in simulation conditions and statically tested on a real vehicle. Results show the correctness of the system architecture and possibility of deployment in real conditions. Full article

Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safety limitations of existing diagnostic robots in factory environments, a hierarchical fuzzy logic-based navigation and obstacle avoidance algorithm is proposed in this study. The algorithm is constructed based on zero-order Takagi–Sugeno type fuzzy control, comprising subfunctions for navigation, static obstacle avoidance, and dynamic obstacle avoidance. Coordinated navigation and equipment protection are achieved by jointly considering the information of the fault point and surrounding equipment. The concept of a dynamic safety boundary is introduced, wherein the normalized breached level is used to replace the traditional distance-based input. In the inference process for dynamic obstacle avoidance, the relative speed direction is additionally considered. A Mamdani-type fuzzy inference system is employed to infer the necessity of obstacle avoidance and determine the priority target for avoidance, thereby enabling multi-objective planning. Simulation results demonstrate that the proposed algorithm can guide the diagnostic robot to within 30 cm of the fault point while ensuring collision avoidance with both equipment and obstacles, enhancing the completeness and safety of the fault point searching process. Full article

Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A conflict-aware global planner, extended from the A* algorithm, generates feasible routes, while a multi-sensor perception stack integrates LiDAR and camera data to distinguish moving AGVs, static obstacles, and task targets. Based on this perception, a Deep Q-Network (DQN) policy with a tailored reward function enables real-time dynamic obstacle avoidance in complex traffic. Simulation results demonstrate that, compared with the Artificial Potential Field (APF) baseline, the proposed GG-DRL approach reduces collisions by ~70%, lowers planning time by 25–30%, shortens paths by 10–15%, and improves smoothness by 20–25%. On the Maze Benchmark Map, GG-DRL surpasses classical planners (e.g., RRT) and deep RL baselines (e.g., DDPG) in path quality, computation, and avoidance behavior, achieving an average path length of 81.12, computation time of 11.94 s, 5.2 avoidance maneuvers, and smoothness of 0.86. Robustness is maintained as a dynamic obstacles scale up to 30. These findings confirm that combining multi-sensor fusion with deep reinforcement learning enhances AGV safety, efficiency, and reliability, with broad potential for intelligent railway logistics. Full article

A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

Under the multiple pressures of intensifying global climate change disruption and rapid economic growth, China has become one of the countries facing the most serious water scarcity problems. Based on the ISO 14046 standard and the framework of water scarcity footprint theory, this study will break through the static limitations and lack of dimensions of traditional characteristic factors (i.e., water stress) and construct a water stress evaluation index system that combines nature, economy, and society. The results indicate that in recent years, regional water stress in China has exhibited significant spatiotemporal variations and spatial clustering, primarily driven by composite factors, with an overall decreasing trend. Among them, Shanghai is the highest-pressure area and Shaanxi is the lowest-pressure area, which is mainly due to the spatial projection of the coupling effect of multi-dimensional factors. In addition, the obstacle degree analysis method shows that indicators such as the utilization rate of water resource development constitute cross-regional constraints. To this end, all regions should make efforts to regulate and control the water use structure, introduce water-saving technologies, and strengthen water-saving publicity according to their needs. Therefore, this study not only provides a scientific basis for in-depth understanding of the distribution law and influencing mechanism of water stress but also provides an important reference for the rational allocation and sustainable use of water resources by upgrading the characteristic factors to system control signals. Full article

In response to the challenges of obstacle avoidance and terrain negotiation encountered by wheel-legged robots in static environments with complex obstacles, this study introduces an enhanced A* path planning algorithm that incorporates a jump-point search strategy, a dynamically weighted heuristic strategy, and a continuous jumping constraint mechanism to facilitate efficient obstacle traversal. The algorithm extends the traditional 8-neighborhood rule to support jumping in the horizontal, vertical, and diagonal directions. A dynamic, weighted heuristic is introduced to adaptively adjust heuristic weights, guide the search direction, improve efficiency, and reduce detours. Redundant point removal and Bézier curve smoothing were employed to enhance path smoothness, whereas the continuous jumping constraint limited the jump frequency and improved motion stability. The results validate that—relative to the standard A* algorithm, which achieves a 73.7% reduction in path nodes (from 54 to 16)—85% fewer search nodes (from 542 to 78) and a planning time of 0.0032 s were achieved while also enhancing performance in crossing complex structures. This enhances the capability of wheel-legged robots to perform real-time path planning in structurally complex yet static environments, thereby improving their autonomous navigation efficiency. Full article

The field of autonomous robotics is progressing rapidly, with research moving toward developing systems capable of moving without direct human control and learning without human intervention. One of the problems requiring an efficient and sustainable solution is ensuring the smooth and safe navigation of robots between obstacles. In this study, a new path planning approach is developed, integrating particle swarm optimization (PSO) and artificial potential field (APF) algorithms to assist the mobile robot in navigating an area with static and dynamic obstacles. The robot moves independently while routing dynamically and avoiding obstacles. To evaluate its adaptive ability to a changing environment, we continuously calculate the shortest distance between two points and dynamically adjust the path to avoid obstacles during replanning, path recalculation, and robot position adjustment to ensure efficient and safe navigation. Different scenarios are tested to evaluate our approach, including different environmental conditions and obstacle configurations. Experimental results show that our method reduces the path length by 18%, the obstacle avoidance efficiency by 90%, and the success rate by 85% in dynamic environments. In addition, PSO-APF reduces computation time, demonstrating better capacity and efficiency. Full article

Continuum robots with outstanding compliance, dexterity, and lean bodies are successfully applied in medicine, aerospace engineering, the nuclear industry, rescue operations, construction, service, and manipulation. However, the inherent low stiffness characteristics of continuum bodies make it challenging to develop ultra-long and small-diameter continuum robots. To address this size–scale challenge of continuum robots, we developed an 8 m long continuum robot with a diameter of 23 mm by a tip actuation and growth mechanism. Meanwhile, we also realized the untethered design of the continuum robot, which greatly increased its usable space range, portability, and mobility. Demonstration experiments prove that the developed growing continuum robot has good flexibility and manipulability, as well as the ability to cross obstacles and search for targets. Its continuum body can transport liquids over long distances, providing water, medicine, and other rescue items for trapped individuals. The functionality of an untethered growing continuum robot (UGCR) can be expanded by installing multiple tools, such as a grasping tool at its tip to pick up objects in deep wells, pits, and other scenarios. In addition, we established a static model to predict the deformation of UGCR, and the prediction error of its tip position was within 2.6% of its length. We verified the motion performance of the continuum robot through a series of tests involving workspace, disturbance resistance, collision with obstacles, and load performance, thus proving its good anti-interference ability and collision stability. The main contribution of this work is to provide a technical reference for the development of ultra-long continuum robots based on the tip actuation and steering principle. Full article

The sustainability of regional development in Europe is deeply influenced by heterogeneous globalization processes, yet the divergent long-term trajectories of these processes remain poorly quantified, hindering the design of targeted policies. This study aims to identify and characterize clusters of European countries with similar patterns of overall globalization development in order to assess implications for sustainable and cohesive growth. A novel clustering algorithm is developed that integrates Dynamic Time Warping with k-means to account for temporal misalignments and capture similarities in development dynamics rather than just static levels. Analysis based on the KOF Globalization Index for 40 countries reveals four distinct clusters: highly globalized and stable Western European economies, converging Central and Eastern European countries, microstates with niche integration models, and a peripheral group of Southeastern European nations facing significant challenges. The results demonstrate a persistent core–periphery divergence in globalization paths across Europe. This divergence presents a major obstacle to achieving territorial cohesion and equitable sustainable development outcomes. Methodologically, this study provides a robust framework for analyzing longitudinal socioeconomic processes. The main conclusion is that a one-size-fits-all EU cohesion policy is insufficient; instead, cluster-specific strategies are necessary in order to mitigate regional inequalities, enhance resilience, and ensure that the benefits of globalization contribute to the goals of sustainable development. The findings offer a quantitative basis for such targeted policy interventions. Full article

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article