Complex Obstacles Research Articles

Peripheric sensors-based leaking source tracking in a chemical industrial park with complex obstacles

Hazardous gas leakage can cause irreversible damage to the environment and human health. When it happens, it's necessary to find the accurate position of the leaking source efficiently and take effective measures to reduce or prevent more irreversible losses. However, source tracking in the scenario with complex obstacles faces the challenge caused by turbulent wind flow. In this paper, ethane leak scenarios with different leaking sources and environmental conditions are simulated using the Flame acceleration simulator (FLACS). Considering that sensors are often deployed at the boundaries of industrial parks for the detection of hazardous gas leakage, the concentration information of these peripheric sensors is mapped to images, which serve as inputs to a convolutional neural network (CNN) to determine the location of the leaking source and wind direction in a chemical industrial park with complex obstacles. The results show the effectiveness of the proposed method. In addition, fixed failure rates of the sensor along with additional meteorological conditions are considered to evaluate the performance of generalization.

Strategy for Reinforcing Students’ Integrity Values through Exemplary and Advice Methods of Darul Ulum Muhammadiyah Islamic Boarding Schools, Kulon Progo


 
 
 Ahlakul karimah (divine character) is the primary criterion for observing and evaluating the outward nature of a person, but various traits and characteristics of children cannot be separated from the influence of their parents or elders. In addition to being required to maintain their children's educational development, parents must be able to recognize and cultivate their children's potential and must not impose their will without first consulting them. Nonetheless, in the contemporary era, education faces increasingly complex obstacles, particularly as the world of online media devices becomes more accessible to the public. In order for a child to realize good morals, he or she must have a model to follow, and educational institutions are the key. This study aims to examine the position of Islamic boarding schools, which are prevalent in Indonesia, particularly on the island of Java, in relation to the level of students' honesty using a method based on emulation and advice from boarding school administrators. The method of this research is a descriptive-analytic qualitative research with a case study approach. The results of study show that the exemplary method in strengthening the values of honesty at the Darul Ulum Muhammadiyah Galur Islamic Boarding School includes two forms of the first method, namely directly through the words and behavior of the management and the second, which indirectly means through stories influence students’ characteristics.
 
 

Learning-based fixed-wing UAV reactive maneuver control for obstacle avoidance

Complex 3D obstacle environments raise high requirements of the rapid reaction capability and safety aiming at the maneuver control for obstacle avoidance (MCOA) of fixed-wing unmanned aerial vehicles (FUAVs). Considering the above demands, a learning-based reactive MCOA framework is proposed in this paper. First, the fundamental controller in this framework composed of the interfered fluid dynamical system (IFDS) guidance law and back-stepping control loops is established. Then, aiming at the rapid reaction requirement, a deep-reinforcement-learning-based (DRL-based) reactive online decision-making mechanism for obstacle avoidance matched with the IFDS guidance law is proposed. And aiming at the high safety requirement, the closed-loop system composed of the above fundamental controller and the FUAV 6-DOF nonlinear dynamic model is introduced into the DRL training environments so that the FUAV state transitions considering the characteristics of controllers and dynamics can be realized, and the corresponding reward functions can be calculated accordingly. On this basis, the optimal guidance instructions with high trackability can be resolved in real time using the actor networks trained by the proposed DRL-based mechanism, and the safe maneuver control in complex obstacle environments can be achieved. Finally, a normative modeling method of DRL training environments matched with the reactive MCOA framework is proposed to promote training efficiency. The effectiveness of the proposed framework is demonstrated by simulations.

Complete coverage path planning algorithm based on energy compensation and obstacle vectorization

A complete coverage path planning (CCPP) algorithm based on energy compensation and obstacle vectorization (ECOV) is proposed. The algorithm can be used in demanding fields such as disinfection robots due to its advantages, such as a low path coverage repetition rate and high coverage rate. The algorithm builds an energy map, classifies various obstacles, and proposes separate special area definitions and corresponding energy reconstruction methods for various types of obstacles. Through the energy compensation of the path and the real-time and non-real-time energy reconstruction of special areas of various obstacles, the robot can adapt to more complicated map models and obtain improved results. The proposed algorithm has strong sensitivity to various complex obstacles. Furthermore, the concept of map parity is proposed. Experimental analysis showed that the algorithm is not sensitive to the map parity of the map model, and the parity will not have a large impact on the algorithm due to the minor changes in the actual environment.

Comparative analysis of cardio-cerebrovascular complications in immigrants and native-born Koreans with diabetes: Risk factors and perspectives

BackgroundGiven the rapidly increasing number of immigrants, it is crucial to address health care issues involving immigrants to facilitate their safe and secure settlement. Especially for common chronic diseases, such as diabetes, immigrants face more complex obstacles to manage their chronic conditions than do native-born residents. Therefore, we aimed to assess differences in the incidence and associated risk factors of cardio-cerebrovascular (CCV) complications of immigrants compared with native-born Koreans with diabetes.MethodsImmigrants and native-born Koreans who had new diagnosis of diabetes and simultaneously received anti-diabetic prescriptions in 2012 were defined by using Korean National Health Insurance Claim Database(KNHICD). CCV complications were assessed at a 3-year follow-up from the index date. We assessed differences in the CCV complications and risk factors using multiple cox regression models.ResultsIn total, 4,008 patients (668 of immigrants and 3,340 of native-born Koreans) who had newly diagnosed diabetes and simultaneously received anti-diabetic prescriptions in 2012 were selected. Immigrants with diabetes were at a 1.39 times higher risk of having CCV complications than native-born Koreans with diabetes (95% CI: 1.021–1.881). Patients who had a usual sources of care (USC) presented a significantly reduced risk of cardio-cerebrovascular complication (HR: 0.452; 95% CI: 0.342–0.598) in both immigrants and native Koreans. In subgroup analysis in immigrants, patients having USC showed decreased risk of CCV incidence (HR: 0.35, 95% CI: 0.175–0.703), whereas >60 years old and Charlson comorbidity index (CCI) score >1 presented increased risk of CCV complications.ConclusionImmigrants with diabetes have a higher risk of CCV complications than native-born Koreans with diabetes. However, having a USC significantly decreased the risk of CCV complications. Therefore, the utilization of USC will benefit to reduce diabetic complications in immigrants as well as reduction of overall health care cost burden, it would be necessary to implement USC in diabetes care at the initial disease stage.

Calculation and Optimization of the Wheel-Track Mobile Robot Reconfi guration Mechanism

The actual problem of an autonomous transport robot design for work in an unstructured environment and in emergency situations is considered. The design of the robot contains a combined system, consisting of transformable track and wheel groups, which allows moving over different types of surfaces, including the conditions of the urban environment, in particular flights of stairs. Movement on a flat surface is carried out only by wheel groups with raised track groups, which provides an increased speed of the robot. Movement on uneven surface is carried out only by track groups, while the wheel groups are raised. It provides increased cross-country ability. Staircase and complex obstacles can be overcome with the simultaneous use of wheel and track groups at angle of the wheel groups relative to the track groups during reconfiguration of geometric shapes and steps of the staircase and obstacles. The robot reconfiguration unit is made in the form of a lever mechanism with electric cylinders, which can be self-locking. It implements the switching of the robot movement modes, as well as lifting the wheel group to the required angle for overcoming obstacles. The analysis of the design and calculation of the lever mechanism of the wheel-track robot is carried out. A kinematic model of the reconfiguration unit has been developed. Relationships between the angles and length of the levers, as well as between the angular velocity of movement of the levers and the speed of movement of the electric cylinder pusher of the lever mechanism are obtained. Optimization of the reconfiguration block operation by creating its mathematical model for programming in the Matlab package is done. The target function and restrictions on the system operation have been determined. As a result, improved mechanical characteristics of the reconstruction unit are obtained.

Effects of Prefrontal Transcranial Direct Current Stimulation on Retention of Performance Gains on an Obstacle Negotiation Task in Older Adults

ObjectivesComplex walking in older adults can be improved with task practice and might be further enhanced by pairing transcranial direct current stimulation (tDCS) to the dorsolateral prefrontal cortex. We tested the hypothesis that a single session of practice of a complex obstacle negotiation task paired with active tDCS in older adults would produce greater within-session improvements in walking performance and retention of gains, compared to sham tDCS and no tDCS conditions. Materials and MethodsA total of 50 older adults (mean age = 74.46 years ± 6.49) with self-reported walking difficulty were randomized to receive either active tDCS (active-tDCS group) or sham tDCS (sham-tDCS group) bilaterally to the dorsolateral prefrontal cortex or no tDCS (no-tDCS group). Each group performed ten practice trials of an obstacle negotiation task at their fastest safe speed. Retention of gains in walking performance was assessed with three trials conducted one week later. Within-session effects of practice and between-session retention effects on obstacle negotiation speed were examined. ResultsAt the practice session, all three groups exhibited significant within-session gains in walking speed (p ≤ 0.005). However, the gains were significantly greater in the sham-tDCS group than in the active-tDCS and no-tDCS groups (p ≤ 0.03) and were comparable between the active-tDCS and no-tDCS groups (p = 0.89). At one-week follow-up, the active-tDCS group exhibited significant between-session retention of gains and continued “offline” improvement in walking speed (p = 0.005). The active-tDCS group showed significantly greater retention of gains than the no-tDCS (p = 0.02) but not the sham-tDCS group (p = 0.24). ConclusionsPairing prefrontal active tDCS with a single session of obstacle negotiation practice may enhance one-week retention of gains in walking performance compared to no tDCS. However, the evidence is insufficient to suggest a benefit of active tDCS over sham tDCS for enhancing the gains in walking performance. Additional studies with a multisession intervention design and larger sample size are needed to further investigate these findings. Clinical Trial RegistrationThe Clinicaltrials.gov registration number for the study is NCT03122236.

Effects of multi-obstacle contexts on obstacle negotiation strategies in healthy older adults under dual-task conditions

BackgroundPerformance of obstacle crossing is an attentionally demanding task due to the need for motor planning and gait regulation, particularly among older adults. Despite extensive studies on age-associated changes in obstacle negotiation strategies, relatively little is known about adaptive mechanisms in the elderly regarding multiple obstacle crossings with different execution demands. Research questionFor better understanding of avoidance strategies employed by the elderly, the current study investigated adaptive mechanisms related to planning and implementation of more complex multi-obstacle contexts. Do older adults use a more conservative strategy such as prolonged step duration or elevated foot height when crossing obstacles with increased task demands of obstacle negotiation? MethodsEleven healthy older and 11 young adults participated in the experiment. We examined how the presence and physical property of the second obstacle influenced the planning and adjustments for obstacle avoidance performance. Spatiotemporal characteristics of the stepping movement were analyzed using a 3D motion capture system. ResultsOlder adults showed a longer stance time before crossing the first obstacle than young adults when the task complexity increased. These stepping characteristics were more evident in the dual-task condition. However, their foot clearance and crossing speed were not influenced by the level of task complexity. SignificanceThese findings suggest that healthy elderly participants may have difficulty in developing the motor plan rather than implementing the stepping strategies under more complex obstacle constraints. A general cognitive decline with advancing age or adaptation of compensatory adjustment to enhance postural stability may underlie such altered obstacle negotiation behaviors in older adults.

Analysis of echolocation behavior of bats in \u201cecho space\u201d using acoustic simulation

BackgroundEcholocating bats use echo information to perceive space, control their behavior, and adjust flight navigation strategies in various environments. However, the echolocation behavior of bats, including echo information, has not been thoroughly investigated as it is technically difficult to measure all the echoes that reach the bats during flight, even with the conventional telemetry microphones currently in use. Therefore, we attempted to reproduce the echoes received at the location of bats during flight by combining acoustic simulation and behavioral experiments with acoustic measurements. By using acoustic simulation, echoes can be reproduced as temporal waveforms (including diffracted waves and multiple reflections), and detailed echo analysis is possible even in complex obstacle environments.ResultsWe visualized the spatiotemporal changes in the echo incidence points detected by bats during flight, which enabled us to investigate the “echo space” revealed through echolocation for the first time. We then hypothesized that by observing the differences in the “echo space” before and after spatial learning, the bats’ attentional position would change. To test this hypothesis, we examined how the distribution of visualized echoes concentrated at the obstacle edges after the bats became more familiar with their environment. The echo incidence points appeared near the edge even when the pulse direction was not toward the edge. Furthermore, it was found that the echo direction correlated with the turn rate of the bat’s flight path, revealing for the first time the relationship between the echo direction and the bat’s flight path.ConclusionsWe were able to clarify for the first time how echoes space affects echolocation behavior in bats by combining acoustic simulations and behavioral experiments.

장애물 조건 맥락의 복잡성에 따른 노인의 장애물 보행 특성 연구

Older and younger adults show a context-dependent effect when performing more than one movements simultaneously. Although age-associated alterations in avoidance strategies when negotiating obstacles have been extensively studied, there is little information regarding how different execution demands of obstacle crossing affect integration and organization processes in older adults. We examined adaptive mechanisms underlying planning and execution characteristics in complex obstacle avoidance environment. Nine healthy older and 9 young adults participated in the experiment. Spatiotemporal characteristics of the stepping movement were analyzed using a 3D motion capture system. Older adults demonstrated a prolonged stance time before crossing the first obstacle than young adults when the task complexity increased. However, other gait variables such as foot clearance and crossing speed were not affected by the level of task complexity. These results indicate that changes in task complexity of obstacle negotiation appeared to alter the planning stage rather than the implementation of obstacle crossing performance in older adults.

Important areas for cetaceans in Russian Far East waters

Abstract Cetacean species are highly mobile, most of them regularly travelling over long distances, thereby presenting complex obstacles to their conservation. Identification of their critical habitats, specifically those parts of a cetacean’s range that are essential for day‐to‐day survival and for maintaining a healthy population growth rate, is necessary for their effective protection. This study presents a summary of the data on cetacean sightings during surveys that covered substantial parts of the Russian Far East coastal waters from the Okhotsk Sea to Chukotka in order to determine important areas for particular cetacean species. Sixteen cetacean species were registered during the surveys, and for 12 of them with sufficient numbers of sightings, zones with maximum sighting rates were identified. Only 13% of all cetacean sightings and 22% of sightings of protected species occurred within marine protected areas (MPAs). The highest sighting rates for protected species were concentrated off north‐eastern Sakhalin Island, in the Shantar Area, in Anadyr Gulf, in Kresta Bay and in the waters off eastern Chukotka. The analysis of the distribution patterns of various cetacean species in Russian Far East seas provides a solid base for future conservation planning. Lack of specific MPAs for protection of cetaceans and associated biodiversity hinders marine conservation in Russian Far East seas. The study highlights the specific zones important for various cetacean species and suggests the extension of some existing MPAs and the creation of new MPAs for future spatial habitat protection measures.

Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space

Hyper-redundant manipulators with multiple degrees of freedom have special application prospects in narrow spaces, such as detection in small spaces in aerospace, rescue on-site disaster relief, etc. In order to solve the problems of complex obstacle avoidance planning and inverse solution selection of a hyper-redundant robot in a narrow space, a cubic B-spline curve based on collision-free trajectory using environmental edge information is planned. Firstly, a hyper-redundant robot composed of four pairs of double UCR (Universal-Cylindrical-Revolute) parallel mechanisms (2R1T, 2 Rotational DOFs and 1 Translation DOF) in series to realize flexible obstacle avoidance motion in narrow space is designed. The trajectory point envelope of a single UCR and the workspace of a single pair of UCR in Cartesian space based on the motion constraint boundaries of each joint are obtained. Then, the constraint control points according to the edge information of the obstacle are obtained, and the obstacle avoidance trajectory in the constrained space is planned by combining the A* algorithm and cubic B-spline algorithm. Finally, a variety of test scenarios are built to verify the obstacle avoidance planning algorithm. The results show that the proposed algorithm reduces the computational complexity of the obstacle avoidance process and enables the robot to complete flexible obstacle avoidance movement in the complex narrow space.

Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

PurposeThe results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators.Design/methodology/approachTo improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results.FindingsSimulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL.Originality/valueThis paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm.Graphical abstractFigure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.

Optimization on planning of trajectory and control of autonomous berthing and unberthing for the realistic port geometry

To realize autonomous shipping, autonomous berthing and unberthing are some of the technical challenges. In the past, numerous research have been done on the optimization of trajectory planning of berthing problems. However, these studies assumed only a simple berth and did not consider obstacles. Optimization of trajectory planning on berthing and unberthing in actual ports must consider the spatial constraints and maintain sufficient distance to obstacles. The main contributions of this study are as follows: (i) a collision avoidance algorithm based on the ship domain which has variable size by the ship speed is proposed, to include the spatial constraints to optimization; (ii) the effect of wind disturbance is taken into account to the trajectory planning to make a feasible trajectory based on the capacity limit of actuators; (iii) showing that the optimization method for berthing is also eligible for the unberthing, which has been almost neglected; (iv) waypoints are included to the optimization process, to make optimization easier on practical applications. The authors tested the proposed method on two existing ports. The proposed method performed well on both the berthing and the unberthing problem and optimized the control input and the trajectory while avoiding collision with the complex obstacles.

Visual Navigation System for Autonomous Drone using Fiducial Marker Detection

Drones have been quickly developing for civilian applications in recent years. Because of the nonlinearity of the mathematical drone model, and the importance of precise navigation to avoid possible dangers, it is necessary to establish an algorithm to localize the drone simultaneously and maneuver it to the desired destination. This paper presents a visual-based multi-stage error tolerance navigation algorithm of an autonomous drone by a tag-based fiducial marker detection in finding its target. Dynamic and kinematic models of the drone were developed by Newton-Euler. The position and orientation of the drone, related to the tag, are determined by AprilTag, which is used as feedback in a closed-loop control system with an Adjustable Proportional-Integral-Derivative (APID) controller. Parameters of the controller are tuned based on steady-State error, which is defined as the distance of the drone from the desired point. The sequence of path trajectory, that drone follows to reach the desired point, is defined as a navigation algorithm. A model of the drone was simulated in a virtual outdoor to mimic hovering in complex obstacles environment. The results present satisfactory performance of the navigation system programmed by the APID controller in comparison with the conventional Proportional-Integral-Derivative (PID) controller. It can be ascertained that the proposed navigation system based on a tag marker in the closed-loop control system is applicable to maneuvering the drone autonomously and useful for various industrial tasks in indoor/outdoor environments.

Denver Encampments and COVID-19: A Geographic Atlas

The purpose of this Atlas is to answer the question, "How has COVID-19 impacted the spatial patterns of the Denver unhoused population in relation to public resources in the city core?" COVID-19 signified a crisis within a crisis for unhoused communities. Exploring the changing shelter situation, closures of public restrooms, and the ongoing sweeps in Denver illustrate the complex obstacles that unhoused individuals continue to experience.

Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.

Evaluation of self-regulated learning on problem-solving skills in online basic Physics learning during the COVID-19 pandemic

The problems of learning physics have experienced increasingly complex obstacles amid the demands of online learning due to the COVID-19 pandemic. The purpose of this study is to explain the basic physics learning process through an online system during a pandemic, by evaluating the Self-Regulated Learning (SRL) approach to Problem-solving Skills (PSS). Data were collected through distributing questionnaires, interviews and documentation studies, then analyzed. This study shows that the applied SRL has been implemented well, but has not been optimal in improving PSS in online Basic Physics learning. The unpreparedness of technological devices and the competence of educators and students become obstacles that result in difficulties in solving physics problems so that the expected results are not following the expected learning targets. Also, this study shows the difficulty of learning physics online during the pandemic. Thus, a responsive physics learning model is needed with conditions that allow the delivery of physics material to be well understood, even though it is delivered through digital media. This is a demand that needs the attention of all parties so that the achievement of online learning targets remains optimal and effective in increasing the problem-solving skills of students during the COVID-19 pandemic.

Barriers to Help Seeking among Victims of Elder Abuse: A Scoping Review and Implications for Public Health Policy in Canada.

Elder abuse is a serious public health concern requiring immediate intervention; however, the under-reporting of elder abuse by victims to formal and informal networks remains a major obstacle. This scoping review aims to identify barriers to help seeking that older adults experiencing abuse confront. The goal is to inform public policies and practices in the Canadian context and identify research gaps in the extant literature. Seven scholarly databases were searched from which 12 articles met the inclusion criteria and were extracted for analysis. The findings from this scoping review revealed three levels at which barriers exist: individual focused, abuser/family focused, and community/culture focused barriers. The results suggest that there are several complex obstacles that older adults face when contemplating disclosure of abuse. Future research into help seeking in the Canadian context should more readily incorporate the voices of elder abuse victim-survivors to develop effective assessment strategies and responsive service provisions.

The Multi-Dimensional Actions Control Approach for Obstacle Avoidance Based on Reinforcement Learning

In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.