Static Environment Research Articles

A 2D path-planning performance comparison of RRT and RRT* for unmanned ground vehicle

In recent years, path-planning has gained significant attention as mobile robots are used in various applications. Several factors determine the optimal path for a mobile robot, including accuracy, length of path, execution time, and turns. Among all planners, sampling-based planners such as rapidly exploring random trees (RRT) and rapidly exploring random trees-star (RRT*) are extensively used for mobile robots. The aim of this paper is the review and performance of these planners in terms of step size, execution time, and path length. All planners are implemented on the Jackal robot in a static environment cluttered with obstacles. Performance comparisons have shown that the reduction of step size results in exploring a greater number of nodes in both algorithms, increasing the probability of each extension succeeding. However, this causes the tree to become denser in both algorithms due to the more explored nodes. The RRT planner requires less execution time when the step size and iteration count are equal to RRT* planners. Moreover, performance plots of both algorithms show that RRT* provides an optimal and smooth path than RRT.

Empirical models for the error induced in piezoelectric ceramic transducer-based acoustic ranging instrumentation and measurement

This article presents experimental methods and analytics for developing statistical models for ultrasonic sensors to improve reliability, repeatability, and signal-to-noise ratio (SNR) in static environments. The experiments use commercial piezoelectric ceramic transducers and over 4100 calibrated readings for three specimens of different materials. Using commercial piezoelectric ceramic transducers, the spatial-temporal approach considers range, environmental factors, material characteristics, and their interactions. A true value approach is used to formulate the error as a function of the distance measured in the time-of-flight (ToF) method at zero azimuth angular directivity. The three statistical models, linear temperature, averaged linear regression, and averaged cubic regression models, are formulated and validated. The most influential parameters in the calibrating distance with an acoustic sensor are the range, coupled effect of temperature, and material characteristics, followed by temperature. The linear model increases the SNR within 0–40 cm from ±20 dB to ≈80 dB, and the cubic model increases the repeatability of the measurement by reducing the absolute error for the entire range of sensors from ±3 cm to less than −1 cm.

Analysis of coping capacities and cognitive biases of novice drivers-A questionnaire-based study.

Coping capacity is a key aspect of driver-vehicle interaction when drivers observe and make decisions, and is of great importance for drivers. However, different drivers have different self-cognition and assess their driving abilities differently, especially for novice drivers. Based on questionnaire data, this study has investigated the coping capacities of drivers in both static environments and dynamic environments. With the ANOVA analysis method and the structural equation model (SEM), this study has verified the effects of gender and driving factors (driving years, driving frequency, driving time) on drivers' coping capacities based on drivers' self-assessment scores and mutual assessment scores. Drivers' self-assessment scores show significant effects of all factors on drivers' coping capacities, and drivers' mutual assessment scores show significant effects of all factors, excluding driving time, on drivers' coping capacities. Also, it has been found that all drivers in the driving year group have cognitive biases. It seems that first-year drivers are always overconfident with their driving skills, while drivers with a driving experience of more than three years usually score driving skills of themselves and other drivers most conservatively. With increased exposure to various traffic conditions, experienced drivers are more aware of their limitations in dealing with complex traffic situations, while novice drivers do not know their lack of capability to properly respond to any unexpected situation they could encounter.

Real-time visual SLAM based YOLO-Fastest for dynamic scenes

Within the realm of autonomous robotic navigation, simultaneous localization and mapping (SLAM) serves as a critical perception technology, drawing heightened attention in contemporary research. The traditional SLAM systems perform well in static environments, but in the real physical world, dynamic objects can destroy the static geometric constraints of the SLAM system, further limiting its practical application in the real world. In this paper, a robust dynamic RGB-D SLAM system is proposed to expand the number of static points in the scene by combining with YOLO-Fastest to ensure the effectiveness of the geometric constraints model construction, and then based on that, a new thresholding model is designed to differentiate the dynamic features in the objection bounding box, which takes advantage of the double polyline constraints and the residuals after reprojection to filter the dynamic feature points. In addition, two Gaussian models are constructed to segment the moving objects in the bounding box in the depth image to achieve the effect similar to the instance segmentation under the premise of ensuring the computational speed. In this paper, experiments are conducted on dynamic sequences provided by the TUM dataset to evaluate the performance of the proposed method, and the results show that the root mean squared error metric of the absolute trajectory error of the algorithm of this paper has at least 80% improvement compared to ORB-SLAM2. Higher robustness in dynamic environments with both high and low dynamic sequences compared to DS-SLAM and Dynaslam, and can effectively provide intelligent localization and navigation for mobile robots.

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

AbstractReal-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

Research on Indoor 3D Reconstruction Technology Based on Semantic Visual Simultaneous Localization and Mapping

In response to the challenge that traditional visual simultaneous localization and mapping (SLAM) systems, based on the assumption of a static environment, struggle to achieve real-time indoor 3D reconstruction in complex dynamic scenes, this paper proposes a real-time indoor 3D reconstruction algorithm based on semantic visual SLAM. By leveraging object detection to obtain 2D semantic information and providing prior information for geometric methods, the fusion of the two effectively suppresses dynamic features, reduces reliance on deep learning methods, and ensures the algorithm's real-time performance. Experimental results on dynamic scenes in the TUM RGB-D dataset show that our algorithm maintains nearly unchanged real-time performance while achieving an average performance improvement of approximately 97.56% and 97.31% on the TUM dataset and Bonn dataset, respectively, compared to the ORB-SLAM2 system. Moreover, our algorithm can reconstruct more intuitive indoor global Octo-map and semantic metric maps compared to sparse point cloud maps, effectively enhancing the scene perception capability of mobile robots and laying the foundation for performing advanced tasks. Furthermore, our algorithm demonstrates a 3.5-10.5 times improvement in real-time performance compared to other mainstream semantic SLAM systems. Experimental results on the NVIDIA Jetson AGX Xavier confirm that our algorithm can run in real time on low-power platforms such as mobile robots or drones. However, the drawbacks of our algorithm include lower reconstruction accuracy in low-texture and large-scale scenes and ineffective suppression of dynamic features in low-dynamic scenes. Future work will consider replacing and improving deep learning methods and integrating IMU and other sensors to enhance system usability.

Lithofacies Characteristics of Gulong Shale and Its Influence on Reservoir Physical Properties

The lithofacies characteristics of the Qingshankou Formation (K2qn) shale in the Gulong Depression are crucial for oil exploration and development. This study investigates the K2qn shale lithofacies characteristics and their impact on reservoir physical properties using scanning electron microscopy (SEM), high-pressure mercury injection (HPMI), and logging quantification. The results indicate that the main minerals in K2qn shale are quartz, plagioclase, and clay. The sedimentary structures are classified into three types: laminated, layered, and massive. The K2qn shale lithofacies can be categorized into 12 types based on a combination of lithology and sedimentary structure. The main types are laminated clayey shale, layered clayey shale, and layered felsic shale. The larger the average pore size of the K2qn lithofacies, the stronger the heterogeneity of pore size distribution in space and the better the pore-to-throat connectivity. The impact of K2qn shale lithofacies on reservoir physical properties is mainly due to differences in lithology, complemented by variations in the sedimentary structural model. Under certain diagenetic or tectonic conditions, a layered sedimentary structural model of lithofacies may not increase reservoir permeability. Generally, felsic and carbonate rocks in tidal flat environments promote the development of shale with high permeability and porosity, while lithofacies deposited in static water environments below the wave base in lake basins typically exhibit low permeability and porosity. The physical properties of a reservoir are primarily influenced by the differences in pore throat characteristics resulting from variations in lithology.

Developing emotional intelligence in a static and interactive music learning environment.

The concept of a static electronic learning environment implies the utilization of tools that do not necessitate any active participation on the part of the learner aside from familiarizing oneself with the provided materials. In contrast, an interactive electronic learning environment necessitates active engagement and interaction from the learner. The research purpose is to compare the impact of interactive and static learning environments on students' emotional intelligence. Music education students (n = 216) took a theoretical course on emotional intelligence during one academic semester at the university. The students were randomly divided into two equal groups: Group A (n = 108) attended the course in a lecture format, using videos and additional reading materials. Group B participated in online forums, completed interactive exercises, and discussed individual personal diaries kept at home with the teacher. At the end of the semester, both groups completed the Mayer-Salovey-Caruso emotional intelligence test. The results suggest that the total score for emotional intelligence was higher in Group B. After another 6 months of training, students passed the same test again to assess the effectiveness of the long-term intervention strategy. The post-training results suggest that Group B has an advantage in training over Group A. At the same time, in the Managing Emotions subcategory, Group B was behind Group A, but after 6 months of training, Group B significantly improved the results (value of p = 0.01). The research summarizes the practical significance of the theoretical course on the development of emotional intelligence among music education students.

Shear Stress and the AMP-Activated Protein Kinase Independently Protect the Vascular Endothelium from Palmitate Lipotoxicity.

Saturated free fatty acids are thought to play a critical role in metabolic disorders associated with obesity, insulin resistance, type 2 diabetes (T2D), and their vascular complications via effects on the vascular endothelium. The most abundant saturated free fatty acid, palmitate, exerts lipotoxic effects on the vascular endothelium, eventually leading to cell death. Shear stress activates the endothelial AMP-activated protein kinase (AMPK), a cellular energy sensor, and protects endothelial cells from lipotoxicity, however their relationship is uncertain. Here, we used isoform-specific shRNA-mediated silencing of AMPK to explore its involvement in the long-term protection of macrovascular human umbilical vein endothelial cells (HUVECs) against palmitate lipotoxicity and to relate it to the effects of shear stress. We demonstrated that it is the α1 catalytic subunit of AMPK that is critical for HUVEC protection under static conditions, whereas AMPK-α2 autocompensated a substantial loss of AMPK-α1, but failed to protect the cells from palmitate. Shear stress equally protected the wild type HUVECs and those lacking either α1, or α2, or both AMPK-α isoforms; however, the protective effect of AMPK reappeared after returning to static conditions. Moreover, in human adipose microvascular endothelial cells isolated from obese diabetic individuals, shear stress was a strong protector from palmitate lipotoxicity, thus highlighting the importance of circulation that is often obstructed in obesity/T2D. Altogether, these results indicate that AMPK is important for vascular endothelial cell protection against lipotoxicity in the static environment, however it may be dispensable for persistent and more effective protection exerted by shear stress.

DESIGN SPECIFICS OF A BUILT-IN DIAGNOSTIC SYSTEM FOR HYDRAULIC MACHINES

The basis for obtaining information about the object of diagnosis is technical diagnostic tools, which involve measuring operations of various parameters, the totality of which is the basis of the diagnostic process. Diagnostic tools are divided into external, portable (mobile) and built-in. Built- in technical diagnostic tools are considered. The built-in diagnostic system (BDS) is an autonomous complex for automatic checking of the degree of operability and serviceability of units and the hydraulic drive as a whole, which allows, within limited limits, to localize some faults based on the results of monitoring diagnostic and functional parameters in operational or special test modes, and the diagnostic results can be presented to the operator or accumulated for further processing. Compared to other built-in diagnostic tools, BDSs are the most complex and relatively new devices. They are in the process of development, prototyping, and experimental research. A set of diagnostic equipment was designed, namely: designing a unit of measuring instrumentation; designing a load device for diagnosing pumps and taking load characteristics directly at the facility. The designed set of diagnostic devices is used as an integrated diagnostic system (due to built-in sensors), as well as a separate diagnostic complex due to the ability to connect external sensors. The values of the diagnostic parameters measured by this complex can be recorded on an internal memory card or transferred via wireless Bluetooth to a PC or Android device (smartphone, tablet) for further processing. A study was also carried out to assess the strength of the hydrotester body using a computational and analytical method (using the finite element method in the Ansys Static Structural environment), which is equivalent to full-scale testing in terms of boundary conditions and achievable results. Based on the calculation results, it is possible to assert that the choice of material and structural dimensions of the designed device were chosen rationally.

Superhydrophobic-electrothermal laser-induced graphene for effectively anti-icing and de-icing

Superhydrophobic-electrothermal hybrid systems have shown perspectives in anti/de-icing fields. However, the hybrid strategy was hindered by complex processes and implementation difficulties. Here, we developed a facile approach to prepare superhydrophobic-electrothermal laminated laser-induced graphene (LIG) coating for achieving highly efficient anti-icing/de-icing. Under the optimized laser parameter, the fluorinated LIG with excellent hydrophobicity (CA of 161.2° and SA of 1.58°) was prepared on composites substrate and prolonged the freezing time from 106 s to 868 s at −15 °C. In addition, we also found that the laminated coating could effectively accomplish anti-icing/de-icing with apparently decreased energy supply compared to electrothermal coating (48.75% in static environment and 17.4% in dynamic environment). This work could contribute to the development of LIG for anti-icing/de-icing applications.

Microstructure and anti-oxidation behavior of laser clad Ni[sbnd]Si coating on molybdenum surface

Ni-Si alloy coatings with various compositions were fabricated on molybdenum surfaces using laser cladding technology. The sound metallurgical bond without cracks and pores between coatings and substrat was revealed.The microstructure and phase composition of the coatings were analyzed using scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The oxidation resistance of the coatings was investigated in a static air environment at 700 °C. The results reveal that the coatings primarily consist of a γ-Ni solid solution, Ni3Si phase, and Mo2Ni3Si phase. Furthermore, the Mo2Ni3Si phase content increases with decreasing Ni content in the powder ratio. The oxidation kinetics curve of the NiSi alloy coatings follows a parabolic law. The oxide layer formed on the coating surface is uniform and dense, comprising SiO2, MoO3, and NiMoO4. Based on the comprehensive analysis of the microstructure of the oxide layer, the Ni90Si10 coating demonstrated superior oxidation resistance

Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings.

This paper uses virtual simulations to examine the interaction between autonomous vehicles (AVs) and their surrounding environment. A framework was developed to estimate the environment's complexity by calculating the real-time data processing requirements for AVs to navigate effectively. The VISTA simulator was used to synthesize viewpoints to replicate the captured environment accurately. With an emphasis on static physical features, roadways were dissected into relevant road features (RRFs) and full environment (FE) to study the impact of roadside features on the scene complexity and demonstrate the gravity of wildlife-vehicle collisions (WVCs) on AVs. The results indicate that roadside features substantially increase environmental complexity by up to 400%. Increasing a single lane to the road was observed to increase the processing requirements by 12.3-16.5%. Crest vertical curves decrease data rates due to occlusion challenges, with a reported average of 4.2% data loss, while sag curves can increase the complexity by 7%. In horizontal curves, roadside occlusion contributed to severe loss in road information, leading to a decrease in data rate requirements by as much as 19%. As for weather conditions, heavy rain increased the AV's processing demands by a staggering 240% when compared to normal weather conditions. AV developers and government agencies can exploit the findings of this study to better tailor AV designs and meet the necessary infrastructure requirements.

Three-dimensional (3D) polylactic acid gradient scaffold to study the behavior of osteosarcoma cells under dynamic conditions.

This study adopts an in vitro method to recapitulate the behavior of Saos-2 cells, using a system composed of a perfusion bioreactor and poly-L-lactic acid (PLLA) scaffold fabricated using the low-cost thermally-induced phase separation (TIPS) technique. Four distinct scaffold morphologies with different pore sizes were fabricated, characterized by Scanning electron microscopy and micro-CT analysis and tested with osteosarcoma cells under static and dynamic environments to identify the best morphology for cellular growth. In order to accomplish this purpose, cell growth and matrix deposition of the Saos-2 osteosarcoma cell line were assessed using Picogreen and OsteoImage assays. The obtained data allowed us to identify the morphology that better promotes Saos-2 cellular activity in static and dynamic conditions. These findings provided valuable insights into scaffold design and fabrication strategies, emphasizing the importance of the dynamic culture to recreate an appropriate 3D osteosarcoma model. Remarkably, the gradient scaffold exhibits promise for osteosarcoma applications, offering the potential for targeted tissue engineering approaches.

Fluorine-free, robust and self-healing superhydrophobic surfaces with anticorrosion and antibacterial performances

Superhydrophobic surfaces with water-repelling ability have important applications, such as self-cleaning, antibacterial and corrosion protection. However, the using of harmful fluorinated materials and its poor mechanochemical stability limit its practical application. Herein, a fluorine-free, robust and self-healing superhydrophobic surface is prepared through a two-step method of laser processing and spraying coating for anticorrosion and antibacterial applications. Laser processing is used to construct periodic micron-sized pillars for obtaining strong interface bonding between coating and substrate by mechanical interlocking effect, and as an ‘armor’, preventing the removal of the coating. The coating consists of epoxy resin (EP), hexadecyltrimethoxysilane (HDTMS) and γ-aminopropyltriethoxysilane treated Cu2O (KH550-Cu2O). The superhydrophobic surface can withstand various mechanical durability tests, such as multiple sandpaper abrasion and tape peeling cycles. It exhibits excellent corrosion inhibition efficiency (ηp > 99 %) on Mg alloy, Tinplate and Al alloy, which results from superhydrophobicity and organic coating. The superhydrophobicity endows surface with excellent antibacterial adhesion performance in a static liquid environment. The bactericidal activity of KH550-Cu2O can effectively inactivate the bacteria in contact with the surface and the free bacteria, providing excellent antibacterial ability in a dynamic liquid environment. It still exhibits good anticorrosion and antibacterial abilities after multiple mechanical abrasion cycles due to the outstanding mechanical durability. Moreover, it exhibits outstanding self-healing ability to plasma etching and oil contamination, self-cleaning ability under air and oil conditions, and chemical stability against acids and alkalis solution. All the above excellent performances promote its application in a wider range of fields.

An adaptive variable neighbourhood search approach for the dynamic vehicle routing problem

In the traditional vehicle routing problem (VRP), a route plan is pre-determined and remains unchanged afterwards. However in practice, several unforeseen events could occur at any point, which cause traffic congestion and delay to the original planned routes. It is therefore important to re-optimise the routes by taking into consideration the real-time information, leading to the Dynamic VRP (DVRP). While most of the DVRP literature mainly focused on the customer requests as the dynamic aspect, this paper, however, concentrates on the dynamic traffic information based on the level of urgency of the accidents. Critical nodes are introduced into the network to provide a diversion opportunity for the en-route vehicle. This novel concept of ‘criticality’ is also more practical than the commonly adopted strategy that allows instantaneous diversion at the current vehicle location. We proposed an adaptive variable neighbourhood search (AVNS) algorithm to generate routes in the static environment which is then adapted accordingly for the dynamic setting. This is a two stage VNS approach with the first one acts as a learning stage whose information is then used in stage 2. Here, a smaller number of neighbourhoods and local searches are chosen at each iteration while adopting a pseudo-random selection procedure derived from stage 1. To provide solution diversity, a large neighbourhood search is also embedded into the search. The flexibility and adaptability of our AVNS approach are demonstrated by the high quality solutions obtained when tested on the commonly used VRP datasets, ranging in size from 50 to 1200 customers, which are modified accordingly. In addition, managerial insights related to the tightness of the routes are also presented and analysed.

Integrating Computer Vision and Photogrammetry for Autonomous Aerial Vehicle Landing in Static Environment

Integrating Computer Vision and Photogrammetry for Autonomous Aerial Vehicle Landing in Static Environment

A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments.

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

Intelligent path planning for cognitive mobile robot based on Dhouib-Matrix-SPP method

The Mobile Robot Path Problem looks to find the optimal shortest path from the starting point to the target point with collision-free for a mobile robot. This is a popular issue in robotics and in this paper the environment is considered as static and represented as a bidirectional grid map. Besides, the novel optimal method Dhouib-Matrix-SPP (DM-SPP) is applied to create the optimal shortest path for a mobile robot in a static environment. DM-SPP is a greedy method based on a column row navigation in the distance matrix and characterized by its rapidity to solve sparse graphs. The comparative analysis is conducted by applying DM-SPP on thirteen test cases and comparing its results to the results given by four metaheuristics the Max-Min Ant System, the Ant System with punitive measures, the A* and the Improved Hybrid A*. The outcomes acquired from different scenarios indicate that the proposed DM-SPP method can rapidly outperform the four predefined artificial intelligence methods.