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To investigate the comprehensive effects of the blocking ratio and the relative position of obstacles on the continuous explosion characteristics of the methane-air mixture, a series of explosion experiments were conducted in a 1.2 m long experimental tube. Methane concentration in the two connecting tubes was maintained at 10–12 vol.%, with obstacle plates featuring varying blocking ratios ( B r ) installed in both sections. Experimental results indicate that variations in the position of the obstacles significantly influence the shape of flame propagation. When the obstacle is positioned in the forepart of the experimental tube, the flame shape evolves through three distinct stages: spherical flame, finger-shaped flame, and vortex flame. Both the maximum flame front speed and the maximum explosion overpressure ( P max ) increase with the increasing blocking ratio. Comparatively, when the obstacle is located in the latter part of the tube, the increased turbulence intensity of the flame leads to the formation of a 'cavity' downstream as the flame interacts with the obstacle. In this scenario, the obstacles have minimal impact on both the flame front speed ( V f ) and the maximum explosion overpressure. The formation of a vortex flame is a direct consequence of the interaction between the flame and the vortex, with flame acceleration occurring as the flame transitions into turbulent combustion due to the influence of the vortex.

The effect of different obstacle positions on the explosion flame dynamics of hydrogen-air mixtures with concentration gradients is investigated by numerical simulations. The numerical simulations predict explosion characteristic parameters matching correctly with the experimental results. The calculation results show that the influence of the obstacle position on the explosion flame of an inhomogeneous mixture is more significant compared to that of a homogeneous mixture. The analysis of the flame morphology and flow field reveals that, as the obstacle position increases, the premixed flame undergoes more complex deformation after passing through the obstacle and generates eddies near the obstacle under the action of vortices. By comparing the flame front position and peak overpressure after the explosion, it is found that, as the obstacle position increases, the concentration gradient has a stronger inhibitory effect on flame propagation. The influence of the concentration gradient on the peak overpressure is more pronounced when the obstacle is 200 mm from the ignition point. This research can provide theoretical guidance for industrial explosion protection and safety management.

Using Complete Coverage Path Planning (CCPP), a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What's more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors’ proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length.

Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies.

The fluid flow and heat transfer around obstacles are an engineering and research interest. This paper dealt with this matter in a porous channel. It explained the features of the presence of hot solid obstacles in porous media. These obstacles were located at different positions inside the medium. The particularity of this work is coupling two complex phenomena: the heat transfer in porous media and the presence of hot solid obstacles. This is the first time that these phenomenona were studied. The diffusion–convection equation is adopted to calculate the temperature. The viscous heat dissipation and compression work due to the pressure were not taken into consideration. This choice and assumptions were based on our previous work. The numerical simulation was done using the generalized lattice Boltzmann method. To ensure that our numerical code is free of errors, we resorted to benchmark cases. Then, we were interested in the effect of triangular and rectangular obstacles on the heat transfer and fluid flow in a porous channel. The isotherms and the velocity contours were studied for several dynamic parameters. The fluid behavior was described by the streamlines and the velocity fields. The velocity profile was followed along the porous channel. These results allow concluding that the Reynolds number increment led to the increase in the heat transfer and the fluid velocity. The increment of the distance between the inlet and the obstacle generates the same conclusion.

The research and innovations in the field of wearable auxiliary devices for visually impaired and blind people are playing a vital role towards improving their quality of life. However, in spite of the promising research outcomes, the existing wearable aids has several weaknesses such as more weight, limitations in the number of features and cost. The main objective of this manuscript is to provide the detailed design of a novel lightweight wearable aid with higher number of features for visually impaired and blind people. The proposed research aims to design a cognitive assistant that will guide the blind people for walking by detecting the environment around them. The framework include a Multi-Sensor Fused Navigation system comprises of a sensor-based, vision-based, and cognitive (intelligent/smart) application. The visual features for the design include obstacle detection, uneven surface detection, slope and downward steps detection, pothole detection and hallow object detection; location tracking, walking guide, image capturing and video recording. This prototype is named as Blind’s Apron based on its appearance. The invention focusses on parameters like reduction on size (quite handy) and light weight (comfortable to wear), higher number of detection features, and minimum user intervention (high end operations like switching on and off). All user interactions are friendly and affordable to everyone. The results obtained in this research lead to a high end technical intervention with ease of use. Finally, the performance of the proposed cognitive assistant is tested with a user study in real-time. The feedback and corresponding results establish the effective outcome of the proposed invention which is a light weight and feature enhanced device with easily understandable instructions.

To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments.

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an average position error of 3.1 cm. During navigation, the robot achieved 98.0% reliability, avoided moving obstacles in 90.0% of encounters, and replanned paths in 0.42 s on average. The integration of LiDAR-based SLAM with deep-learning–driven semantic perception establishes a robust foundation for intelligent, adaptive, and safe robotic navigation in dynamic environments.

Abstract To address dynamic obstacle avoidance planning in multi-robot coordinated suspension systems (MCSS), this study proposes a hybrid method integrating an enhanced stable dung beetle optimization (SDBO) algorithm with an improved dynamic window approach (IDWA). Dynamic obstacles are addressed through IDWA-based trajectory prediction, while the SDBO–IDWA algorithm optimizes obstacle avoidance trajectories for suspended objects. Furthermore, leveraging force–position cooperative optimization, the method resolves coupled kinematic and dynamic constraints inherent in MCSS. Simulation and experimental results demonstrate that the SDBO–IDWA algorithm outperforms traditional approaches, achieving a 19.95% reduction in minimum trajectory length and a 57.77% decrease in runtime for suspended objects. For towing robots, it reduces optimal trajectory length by 9.52% and fitness values by 9.44%. The findings advance planning theory and enable safe, stable multi-robot suspension systems for diverse towing applications. Graphical Abstract Graphical Abstract