Complex Obstacles Research Articles

Robotic Microcapsule Assemblies with Adaptive Mobility for Targeted Treatment of Rugged Biological Microenvironments.

Microrobots are poised to transform biomedicine by enabling precise, noninvasive procedures. However, current magnetic microrobots, composed of solid monolithic particles, present fundamental challenges in engineering intersubunit interactions, limiting their collective effectiveness in navigating irregular biological terrains and confined spaces. To address this, we design hierarchically assembled microrobots with multiaxis mobility and collective adaptability by engineering the potential magnetic interaction energy between subunits to create stable, self-reconfigurable structures capable of carrying and protecting cargo internally. Using double emulsion templates and magnetic control techniques, we confine 10 nm iron oxide and 15 nm silica nanoparticles within the shell of 100 μm microcapsules that form multiunit robotic collectives. Unexpectedly, we find that asymmetric localization of iron oxide nanoparticles in the microcapsules enhances the intercapsule potential energy, creating stable connections under rotating magnetic fields without altering the magnetic susceptibility. These robotic microcapsule collectives exhibit emergent behaviors, self-reconfiguring into kinematic chain-like structures to traverse complex obstacles, arched confinements, and adhesive, rugged biological tissues that typically impede microscale systems. By harnessing these functions, we demonstrate targeted antifungal delivery using a localized biofilm model on mucosal tissues, showing effective killing ofCandida without binding or causing physical damage to host cells. Our findings show how hierarchical assembly can produce cargo-carrying microrobots with collective, self-adaptive mobility for traversing complex biological environments, advancing targeted delivery for biomedical applications.

Obstacle avoidance algorithm in mobile robot motion

A method has been proposed, and a computer program has been developed for simulating obstacle avoidance by a robotic system. To expand the mobility capabilities of the robotic system, the algorithm combines the core elements of the vfh algorithm and our previously developed algorithm I [1], and is positioned in this work as algorithm II. An evaluation of the average obstacle avoidance time for various types of obstacles has been conducted, including five cubic obstacles, a long wall obstacle, and a complex obstacle. During the computational experiment using the Gazebo 11 simulation environment, the time parameters for robot movements around various types of obstacles were calculated, including five cubic obstacles, a wall obstacle, and a complex obstacle. In the computational experiment, statistical processing of the obtained results was carried out. It was shown that using the proposed algorithm, the assessment of the average time for the robotic system to avoid obstacles is reduced in some cases by up to 7.2 times compared to the use of algorithm I.

A Global Coverage Path Planning Method for Multi-UAV Maritime Surveillance in Complex Obstacle Environments

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments.

Enhanced Nutcracker Optimization Algorithm with Hyperbolic Sine-Cosine Improvement for UAV Path Planning.

Three-dimensional (3D) path planning is a crucial technology for ensuring the efficient and safe flight of UAVs in complex environments. Traditional path planning algorithms often find it challenging to navigate complex obstacle environments, making it challenging to quickly identify the optimal path. To address these challenges, this paper introduces a Nutcracker Optimizer integrated with Hyperbolic Sine-Cosine (ISCHNOA). First, the exploitation process of the sinh cosh optimizer is incorporated into the foraging strategy to enhance the efficiency of nutcracker in locating high-quality food sources within the search area. Secondly, a nonlinear function is designed to improve the algorithm's convergence speed. Finally, a sinh cosh optimizer that incorporates historical positions and dynamic factors is introduced to enhance the influence of the optimal position on the search process, thereby improving the accuracy of the nutcracker in retrieving stored food. In this paper, the performance of the ISCHNOA algorithm is tested using 14 classical benchmark test functions as well as the CEC2014 and CEC2020 suites and applied to UAV path planning models. The experimental results demonstrate that the ISCHNOA algorithm outperforms the other algorithms across the three test suites, with the total cost of the planned UAV paths being lower.

Numerical Simulation Study on the Diffusion Characteristics of High-Pressure Hydrogen Gas Leakage in Confined Spaces

Hydrogen, as one of the most promising renewable clean energy sources, holds significant strategic importance and vast application potential. However, as a high-energy combustible gas, hydrogen poses risks of fire and explosion in the event of a leakage. Hydrogen production plants typically feature large spatial volumes and complex obstacles, which can significantly influence the diffusion pathways and localized accumulation of hydrogen during a short-term, high-volume release, further increasing the risk of accidents. Implementing effective hydrogen leakage monitoring measures can mitigate these risks, ensuring the safety of personnel and the environment to the greatest extent possible. Therefore, this paper uses CFD methods to simulate the hydrogen leakage process in a hydrogen production plant. The study examines the molar fraction distribution characteristics of hydrogen in the presence of obstacles by varying the ventilation speed of the plant and the directions of leakage. The main conclusions are as follows: enhancing ventilation can effectively prevent the rapid increase in hydrogen concentration, with higher ventilation speeds yielding better suppression. After a hydrogen leak in a confined space, hydrogen tends to diffuse along the walls and accumulate in corner areas, indicating that hydrogen monitoring equipment should be placed in corner locations.

Obstacle Avoidance of Surface Agent Formation Based on Streamline Traction at Fixed-Time

The marine environment is highly complex and variable, featuring obstacles such as islands, buoys, and vessels. Safe navigation of the surface agent (SA) fleet is crucial for ensuring the safety of the SA fleet, enhancing operational efficiency, and guaranteeing the smooth execution of the fleet’s mission. Regarding the problem of formation obstacle avoidance for SA fleets encountering complex obstacles during navigation, this chapter presents a fixed-time-based safe navigation algorithm for the SA fleet based on streamline traction. Firstly, to precisely position each SA at the designated location within the formation, a highly malleable leader–follower formation mode is introduced. Based on an enhanced interfered fluid dynamical system (EIFDS) obstacle avoidance algorithm, the virtual Leader is guided to evade static obstacles and determine a trajectory of the designated position. Secondly, a first-order fixed-time control Lyapunov function (FTCLF) is designed based on the EIFDS obstacle avoidance algorithm to guide the angular velocity constraint. The optimal guiding angular velocity signal is obtained through quadratic programming, ensuring that the SA steers towards the designated position while avoiding obstacles. Next, for the guiding velocity amplitude signal, a first-order fixed-time control barrier function (FTCBF) is designed based on the streamline formation scheme and the inter-boat safety distance to guide the velocity amplitude constraint. The optimal guiding velocity amplitude signal is obtained through quadratic programming, guaranteeing that each SA maintains the formation while avoiding collisions with adjacent vessels. Finally, the simulation results indicate the effectiveness, superiority, and stability of the proposed fixed-time-based safe navigation guidance algorithm for the SA fleet based on streamline traction.

Evaluation of the Obstacles Encountered by South African International Students in Tertiary Educational Institutions

The South African higher education system has seen a gradual rise in the number of international students, mainly from African countries but not exclusively confined to other nations. Although the number of international students has significantly risen in recent decades, there is less knowledge about the obstacles they face and the potential effect of these issues on them. This research aims to comprehensively evaluate the difficulties encountered by international students at higher education institutions, providing insight into the complex obstacles that affect their academic, social, and cultural assimilation. The study utilizes a qualitative approach, incorporating a questionnaire as the research design to collect perspectives from international students. The findings indicate that communication, finances, environment, access to resources, and administration are factors that contribute to the obstacles encountered by international students. This highlights the necessity for specific support mechanisms and policy adjustments to improve the overall educational experience for this expanding demographic. The results of this assessment provide important insights for educational institutions, governments, and stakeholders that want to promote inclusive and supportive settings that facilitate the achievement and well-being of international students in higher education.

Developing smart archives in society 5.0: Leveraging artificial intelligence for managing audiovisual archives in Africa

In the age of Society 5.0, marked by the profound integration of digital technologies and human society, the preservation and management of audiovisual archives in Africa face substantial challenges. The application of artificial intelligence plays a pivotal role in the establishment of digital archives in the society 5.0. The exponential growth of digital content, combined with the continent's diverse cultural landscape, presents complex obstacles to conventional archival practices. Without the effective implementation of artificial intelligence technologies, crucial aspects such as curation, accessibility and sustainable management of audiovisual archives in Africa remain hindered. Thus, the overarching problem under scrutiny pertains to how the establishment of intelligent archives, facilitated by AI, can offer innovative solutions for preserving and managing audiovisual archives in Africa, within the overarching framework of Society 5.0. This approach has the potential to significantly contribute to safeguarding Africa's rich cultural heritage and advancing research, education and cultural exchange. The purpose of this paper will be to focus on the challenges encountered by selected African countries, such as the ESARBICA countries and those that undertake environmental conservation projects, in managing their audiovisual collections and what can be done to facilitate AI. It resulted in the formulation of a framework for implementing smart archives using AI in African contexts, emphasizing practical strategies and best practices. A qualitative approach enabled a nuanced understanding of complex dynamics at play. Through the observation of AI integration processes, by means of desktop analyses of websites of African national archives and online interest groups showcasing nature conservation projects and initiatives, this article aims to capture a comprehensive picture of the current landscape and future possibilities.

Impact of Obesity, Menopause, and Depression in Women's Health: An Attempt to Decipher the Complex Relationship.

Menopause symptoms may be distressing, especially when they appear at a time when women are expected to play significant responsibilities in society. Numerous biological systems are influenced by the hormonal changes that start during the menopausal transition. This review attempts to decipher the complex relationship between obesity, menopause, and depression, citing some recent longitudinal and cross-sectional studies. Additionally, this study provides a summary of the different phytoestrogens, their sources, and probable mechanisms of action in addition to available therapeutic alternatives. For this review purpose, the authors have gone through a vast number of articles from various scientific databases like PubMed, Google Scholar, and Web of Science. It is becoming clear that the physiological basis for these menopausal symptoms is complicated and connected to estrogen deficiency, but not alone. Other hormones like FSH, LH, progesterone, and inhibin B are the major ones that are both directly and indirectly responsible for most of the menopausal symptoms. Numerous longitudinal and cross-sectional studies have found a direct relationship between the incidence of menopause and depression as well as obesity. Phytoestrogens like stilbene, lignans, isoflavone, and coumestan have been reported to be the alternatives to synthetic estrogen with lesser side effects, as reported in various studies. The complex relationship between depression, menopause, and obesity presents a complex obstacle to women's health and overall well-being. There might be a lot of promising prospects for revolutionary advancements in women's health during the menopausal stage in the future. Promising drug development that targets not just one but also the three conditions -obesity, menopause, and depression - as well as more thorough research are needed to improve the healthcare system for women who suffer from these conditions.

An Innovative Path Planning Algorithm for Complex Obstacle Environments with Adaptive Obstacle Density Adjustment: AODA-PF-RRT*

To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.

Capture behavior of self-propelled particles into a hexatic ordering obstacle

Abstract Computer simulations are utilized to investigate the dynamic behavior of self-propelled particles (SPPs) within a complex obstacle environment. The findings reveal that SPPs exhibit three distinct aggregation states within the obstacle, each contingent on specific conditions. A phase diagram outlining the aggregation states concerning self-propulsion conditions is presented. The results illustrate a transition of SPPs from a dispersion state to a transition state as persistence time increases within the obstacle. Conversely, as the driving strength increases, self-propelled particles shift towards a cluster state. A systematic exploration of the interplay between driving strength, persistence time, and matching degree on the dynamic behavior of self-propelled particles is conducted. Furthermore, an analysis is performed on the spatial distribution of SPPs along the y-axis, capture rate, maximum capture probability, and mean-square displacement. The insights gained from this research make valuable contributions to understanding the capture and collection of active particles.

Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices

The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective.

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Modifying supply chain strategies to address regulatory compliance challenges: Lessons from South Eastern Nigeria

Regulatory compliance was an essential component of contemporary corporate operations, guaranteeing conformity to laws, rules, and standards controlling different aspects of organisational activity. Businesses operating in South Eastern Nigeria encounter complex obstacles to regulatory compliance, such as currency depreciation, the dynamic nature of data protection legislation, and the increasing global integration of supply chains. This report offers valuable insights into the intricate process of adjusting supply chain strategies to address regulatory compliance difficulties in South Eastern Nigeria. The paper examined regulatory compliance difficulties and their effects on supply chain dynamics by conducting a thorough literature review and analysing current research. Devaluing currency led to changes in import/export levies, tariffs, and customs procedures, which need quick and adaptable actions to ensure compliance and competitiveness. In addition, the digitalization of corporate operations intensified the issues of ensuring data security and privacy compliance. This necessitated investing in technological solutions and adopting proactive risk management strategies. Despite these difficulties, firms in South Eastern Nigeria adopted a range of tactics, such as implementing compliance management systems, diversifying suppliers and markets, and engaging with regulatory agencies, to successfully modify their supply chain strategies. The results emphasised the significance of taking proactive actions, engaging in strategic planning, and fostering cooperation among relevant parties to effectively address regulatory compliance obstacles and maintain competitiveness in the global market. Keywords: Adherence to regulations, Techniques for managing supply chains, Depreciation of currency, Safeguarding data, Region of South Eastern Nigeria.

An improved RRT behavioral planning method for robots based on PTM algorithm

For multi-dimensional high-order nonlinear systems with unstable path quality in parameter and extension terms, we developed a new fast search random tree strategy. First, we established a high-order Lipschitz vector field dynamic system to adapt to high-order systems of multi-degree-of-freedom robots, with the complex obstacle function being one of its key components. Secondly, we designed a classification gap filtering network layer (Classification LSTM) to screen training data models and ensure the global stability of data in path design. Additionally, the visual sensors deployed in the unit area effectively implement the path marking backtracking strategy and dead zone path simplification. Finally, three examples are provided to verify the effectiveness of this design method.

Hybrid Optimization Path Planning Method for AGV Based on KGWO.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method's potential in real-world applications.

Navigating classroom challenges and curriculum changes: A qualitative study of an English Teacher’s journey in the Indonesian education system

This article employs narrative inquiry to investigate the experiences of an English teacher, referred to as Dede (pseudonym), at a senior high school in West Java, Indonesia. The research question guiding this study is: ‘How does an English teacher in senior high school navigate and adapt his teaching approaches in response to curriculum changes in Indonesia?’ Using a semi-structured interview, this study provides a detailed analysis of Dede’s narratives to uncover the challenges faced by English educators in this setting and to highlight the sources of their motivation and dedication. The research focuses on four main themes: teaching journey; public versus private schools; curriculum stories; and policy and practice. The findings reveal the complex obstacles and dynamic changes in curriculum that English teachers navigate, offering valuable insights into the broader context of senior secondary education in Indonesia. By exploring Dede’s teaching path, this study contributes to the ongoing discussion of effective teaching strategies and the evolving landscape of English language education in West Java.

Multi-agent flocking control with complex obstacles and adaptive distributed convex optimization

The optimization issue involving flocking control and obstacle avoidance behavior in continuous-time multi-agent systems is the main topic of this work. When an agent encounters an obstacle, a virtual agent is introduced to generate repulsive force, enabling the multi-agent system to navigate around obstacles smoothly. Under the local interaction between agents, a multi-agent flocking control and obstacle avoidance algorithm is proposed with utilizing adaptive distributed convex optimization. This algorithm can minimize the sum of local costs of the system. A symbolic function-based estimation method is introduced to effectively relax the constraints of the cost function. The Lyapunov approach is employed to show the stability of the multi-agent system. With the proposed algorithm, each agent can achieve flocking behavior for convex optimization in complex obstacle environments by interacting with their neighbors, virtual agents, and virtual leaders. Last but not least, simulation examples are given to further illustrate the effectiveness and efficiency of the suggested control algorithm.

Global Path Planning of Unmanned Surface Vehicle in Complex Sea Areas Based on Improved Streamline Method

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios.

Implementing dog-like quadruped robot turning motion based on key movement joints extraction

For biomimetic robots to be used in the real world, it is very important to have animal-like turning motion. Excellent turning ability can help quadruped robots navigate more complex obstacle terrain. To solve this problem, we conducted the corresponding study by collecting multiple sets of data on different movements of Marinua dogs. First, we extracted the key movement joints of dogs and captured the motion trajectories of the key joints by a reduced order model. Then, on the basis of systematic analysis and mathematical modeling of the experimental dog’s turning motion, we propose an N-step turning strategy to enable the quadruped robot to realize a wide range of multi-angle movements. Meanwhile, based on the extracted joint trajectory and considering the influence of the actual turning speed, radius and stability of the quadruped robot, the more reasonable biomimetic turning trajectory is obtained through optimization. In terms of motion control, we propose a virtual spine control method to realize turning motion, and plan the foothold adjustment according to different speeds. Finally, experiments show that the proposed method can effectively realize multi-angle turning and U-turn and O-circle turning motion of quadruped robot, which proves the effectiveness of the proposed method. At the same time, the biomimetic turning control method proposed in this paper can be easily extended to other biomimetic robot movements inspired by other organisms.