• All Solutions All Solutions
    • Editage

      One platform for all researcher needs

    • Paperpal

      AI-powered academic writing assistant

    • R Discovery

      Your #1 AI companion for literature search

    • Mind the Graph

      AI tool for graphics, illustrations, and artwork

    Unlock unlimited use of all AI tools with the Editage Plus membership.

    Explore Editage Plus
  • Support All Solutions
    discovery@researcher.life
Discovery Logo
Paper
Search Paper
Cancel
Ask R Discovery
Features
  • Top Papers
  • Library
  • audio papers link Audio Papers
  • translate papers link Paper Translation
  • translate papers link Chrome Extension
Explore

Content Type

  • Preprints
  • Conference Papers
  • Journal Articles

More

  • Research Areas
  • Topics
  • Resources

Obstacles In Environment Research Articles

  • Share Topic
  • Share on Facebook
  • Share on Twitter
  • Share on Mail
  • Share on SimilarCopy to clipboard
Follow Topic R Discovery
By following a topic, you will receive articles in your feed and get email alerts on round-ups.
Overview
1016 Articles

Published in last 50 years

Related Topics

  • Dynamic Obstacles
  • Dynamic Obstacles
  • Obstacle Avoidance
  • Obstacle Avoidance
  • Unknown Environment
  • Unknown Environment

Articles published on Obstacles In Environment

Authors
Select Authors
Journals
Select Journals
Duration
Select Duration
998 Search results
Sort by
Recency
DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments

DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments

Read full abstract
  • IEEE Transactions on Automation Science and Engineering
  • Jan 1, 2024
  • Bo Chen + 6
Cite
Save

A user-led audit of the walkability and wheelability of Quebec City’s neighborhoods by mobility assistive technology users

For mobility assistive technology (MAT) users, environmental obstacles or helpful elements can make the difference between disabling situations and social participation. User led environmental evaluations can highlight difficulties MAT users experience, which can inform changes to the built and social environment. This study used the Stakeholders’ Walkability/Wheelability Audit in Neighbourhood People with Disabilities (SWAN-PDW) to identify observable (objective) and experienced (subjective) barriers/obstacles and facilitators/helpful features encountered by 25 MAT users in their daily lives in three residential environments (i.e. urban, semi-urban and suburban) in Quebec City (Canada). Because the participants’ were directly involved in the identification of obstacles and helpful elements, this type of user-led evaluation may empower MAT users to initiate discussions with the relevant authorities. By acknowledging the difficulties and opportunities encountered by MAT users, stakeholders can use these individuals’ expertise in the planning and decision-making processes to improve access for all citizens.

Read full abstract
  • Disability & Society
  • Dec 30, 2023
  • François Routhier + 6
Cite
Save

An Integrated Autonomous Dynamic Navigation Approach toward a Composite Air-Ground Risk Construction Scenario.

Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air-ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated potential-field-based risk assessment approach is proposed to evaluate the threat severity of the environmental obstacles. Meanwhile, the self-adaptive dynamic window approach is suggested to manage the real-time motion planning solution for air-ground risks. By designing the multi-objective velocity sample window, we constrain the vehicle's speed planning instructions within reasonable limits. Combined with a hierarchical decision-making mechanism, this approach achieves effective obstacle avoidance with multiple drive modes. Simulation results demonstrate that, in comparison with the traditional dynamic window approach, the proposed method offers enhanced stability and efficiency in risk avoidance, underlining its notable safety and effectiveness.

Read full abstract
  • Sensors (Basel, Switzerland)
  • Dec 30, 2023
  • Da Jiang + 7
Open Access
Cite
Save

A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

Read full abstract
  • Machines
  • Dec 29, 2023
  • Jiabin Yu + 6
Open Access
Cite
Save

Multi-objective reinforcement learning for autonomous drone navigation in urban areas with wind zones

Drones can be used for tasks such as data collection and logistics in civil engineering. Current research on autonomous drones mainly focuses on planning a safe path and avoiding obstacles in a static environment. However, navigating a drone in complex environments like urban areas involves many dynamic constraints, such as building layout, winds, and signal coverage, which are interdependent. The wind factor is the most important among these environmental factors, which may cause a drone to lose control or even crash. This paper presents a multi-objective navigation reinforcement learning algorithm (MONRL) for the drone to navigate and avoid obstacles in an unknown environment when dynamic wind zones present, with only imagery data about the building layouts. Based on a deep reinforcement learning and memory architecture, the drone develops policies to prioritize navigation decisions, optimizing the path while minimizing negative impact of winds with only sparse sensor data, in our case, camera inputs. By leveraging the advantages of the proposed method in estimating the environmental factors from previous trials, no aerodynamic force sensors are needed for the drone to develop effective strategies to navigate to target while counteracting to milder winds and dodging aways from stronger winds. The proposed method was tested in a virtual environment, and a real model of New York City. The method is expected to contribute to new automation algorithms for urban aerial logistics and future automated civil infrastructure inspection.

Read full abstract
  • Automation in Construction
  • Dec 27, 2023
  • Jiahao Wu + 2
Cite
Save

Velocity obstacle guided motion planning method in dynamic environments

The ability of a robot to navigate through a dynamic environment, avoid obstacles, and reach its destination is crucial for safety and a major technological challenge in autonomous navigation, especially in crowded environments involving hospitals, hotels, and restaurants. Most prominent methods use distance metrics as a safety constraints in their planning algorithms, which leads to overly cautious navigation. Thus, a velocity obstacle guided motion planning method for mobile robots in moving obstacle environments is proposed. The approach automatically negotiates dynamic obstacles combining velocity obstacle (VO) and trajectory generating. First, a dynamic perception algorithm is developed to track and predict obstacles using only point cloud input. Subsequently, to obtain an initial trajectory that satisfies kinodynamic and the VO-based safety metric (VOSM), VO is applied to search the kinodynamic path to verify the safety of the extended motion primitives. Finally, the smoothness and safety of the robot trajectory are enhanced by nonlinear optimization, which incorporates the proposed safety constraints based on VO. Extensive simulations in challenging environments demonstrate a 40.0% success rate increase and a 44.8% trajectory smoothness improvement over obstacle distance-based methods. Practical testing prove our technology is reliable and can safely avoid dynamic obstacles.

Read full abstract
  • Journal of King Saud University - Computer and Information Sciences
  • Dec 23, 2023
  • Wanxin Liu + 4
Open Access
Cite
Save

An obstacle detection method for dual USVs based on SGNN-RMEN registration of dual-view point clouds

This paper proposes an unsupervised obstacle detection method for dual unmanned surface vessels (USVs) using the SGNN-RMEN framework. The method utilizes dual-view point clouds captured by two USVs to improve the detection of small obstacles under the condition of a visible shore. Firstly, a siamese graph neural network (SGNN) is designed to extract global contour features. A specialized task of global contour consistency classification is employed to ensure the interpretability of these features. Secondly, a rotation matrices estimation network (RMEN) is utilized to estimate the optimal rotation transformation for aligning point clouds based on the global contour features of the dual-view point clouds. The overlap degree between the dual-view point clouds before and after registration is used as feedback to train the model, enabling unsupervised learning. Finally, a " gridding - filtering - clustering” method is applied to annotate the size and position of obstacles based on the registration of the dual-view point clouds. Comparative experiments on two datasets demonstrate the effectiveness of the proposed method in accurately extracting contour features, achieving precise dual-view point cloud registration, and improving the detection rate of small obstacles in nearshore environments. The method also exhibits strong adaptability in unfamiliar marine environments.

Read full abstract
  • Ocean Engineering
  • Dec 21, 2023
  • Zehao He + 4
Cite
Save

Real-time constraint-based planning and control of robotic manipulators for safe human–robot collaboration

A recent trend in industrial robotics is to have robotic manipulators working side-by-side with human operators. A challenging aspect of this coexistence is that the robot is required to reliably solve complex path-planning problems in a dynamically changing environment. To ensure the safety of the human operator while simultaneously achieving efficient task realization, this paper introduces a computationally efficient planning and control architecture that combines a Rapidly-exploring Random Tree (RRT) path planner with a trajectory-based Explicit Reference Governor (ERG) by means of a reference selector. The resulting scheme can steer the robot arm to the desired end-effector pose in the presence of actuator saturation, limited joint ranges, speed limits, a cluttered static obstacle environment, and moving human collaborators. The effectiveness of the proposed framework is experimentally validated on the Franka Emika Panda robot arm and fed with feedback information from state-of-the-art depth perception systems. Our method outperforms both the standalone RRT and ERG algorithms in cluttered static environments where it overcomes: i) the RRT’s inability to handle dynamic constraints which result in constraint violations and ii) the ERG’s undesirable property of getting trapped in local minima. Finally we employed the RRT+ERG in highly dynamic human–robot coexistence experiments without sacrificing the real-time requirements.

Read full abstract
  • Robotics and Computer-Integrated Manufacturing
  • Dec 21, 2023
  • Kelly Merckaert + 3
Cite
Save

Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm.

This paper proposes an improved Soft Actor-Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory depreciation issue caused by resetting the LSTM's hidden states to zero during training, a burn-in training method was adopted to boost the performance. Moreover, a prioritized experience replay mechanism was implemented to enhance sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a motion model for the Turtlebot3 mobile robot was established by designing the state space, action space, reward function, and overall planning process. Three simulation experiments were conducted in obstacle-free, static obstacle, and dynamic obstacle environments using the ROS platform and Gazebo9 software. The results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM algorithm demonstrated a faster convergence rate and a higher path planning success rate, registering a significant 10.5 percentage point improvement in the success rate of reaching the target point in the dynamic obstacle environment. Additionally, the time taken for path planning was shorter, and the planned paths were more concise.

Read full abstract
  • Sensors
  • Dec 13, 2023
  • Yongchao Zhang + 1
Open Access
Cite
Save

Device-Free Localization Techniques: A Review

Device-free localization (DFL) has emerged as a transformative technology for tracking objects and individuals without requiring them to carry electronic devices. This paper reviews two pivotal techniques of DFL: Link Quality Measurement and Link Scattering techniques. Link Quality Measurement focuses on evaluating the quality of wireless links through metrics like Received-Signal-Strength and Channel-State-Information, offering simplicity and reliability. Meanwhile, Link Scattering harnesses signal reflections and diffractions caused by environmental obstacles to estimate device-free target positions. This review provides insights of Link Quality Measurement, Link Scattering methods and highlighting DFL metrics. By shedding light on these critical aspects, it offers valuable insights into the current state of DFL technology and its potential in diverse domains, ranging from smart environments to security systems.

Read full abstract
  • Journal of Techniques
  • Dec 7, 2023
  • Abd Al-Rahman Tariq Rasheed + 3
Open Access
Cite
Save

Identifying locally actionable strategies to increase participant acceptability and feasibility to participate in Phase I cancer clinical trials.

Recruitment of cancer clinical trial (CCT) participants, especially participants representing the diversity of the US population, is necessary to create successful medications and a continual challenge. These challenges are amplified in Phase I cancer trials that focus on evaluating the safety of new treatments and are the gateway to treatment development. In preparation for recruitment to a Phase I recurrent head and neck cancer (HNC) trial, we assessed perceived barriers to participation or referral and suggestions for recruitment among people with HNC and community physicians (oncologist, otolaryngologistor surgeon). Between December 2020 and February 2022, we conducted a qualitative needs assessment via semistructured interviews with a race and ethnicity-stratified sample of people with HNC (n = 30: 12 non-Hispanic White, 9 non-Hispanic African American, 8 Hispanicand 1 non-Hispanic Pacific Islander) and community physicians (n = 16) within the University of Florida Health Cancer Center catchment area. Interviews were analyzed using a qualitative content analysis approach to describe perspectives and identify relevant themes. People with HNC reported thematic barriers included: concerns about side effects, safetyand efficacy; lack of knowledgeand systemic and environmental obstacles. Physicians identified thematic barriers of limited physician knowledge; clinic and physician barriersand structural barriers. People with HNC and physicians recommended themes included: improved patient education, dissemination of trial informationand interpersonal communication between community physicians and CCT staff. The themes identified by people with HNC and community physicians are consistent with research efforts and recommendations on how to increase the participation of people from minoritized populations in CCTs. This community needs assessment provides direction on the selection of strategies to increase CCT participation and referral. This study focused on people with HNC and community physicians' lived experience and their interpretations of how they would consider a future Phase I clinical trial. In addition to our qualitative data reflecting community voices, a community member reviewed the draft interview guide before data collection and both people with HNC and physicians aided interpretation of the findings.

Read full abstract
  • Health expectations : an international journal of public participation in health care and health policy
  • Dec 2, 2023
  • Stephanie A S Staras + 14
Open Access
Cite
Save

Distributed Large-Scale Swarm Control in Obstacle Environment Based on Random Finite Set Theory

Distributed Large-Scale Swarm Control in Obstacle Environment Based on Random Finite Set Theory

Read full abstract
  • IEEE Transactions on Aerospace and Electronic Systems
  • Dec 1, 2023
  • Jianjun Sun + 4
Cite
Save

Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking

The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.

Read full abstract
  • Journal of Marine Science and Engineering
  • Nov 30, 2023
  • Wei Li + 3
Open Access
Cite
Save

INVys: Indoor Navigation System for Persons with Visual Impairment Using RGB-D Camera

This research presents the INVys system aiming to solve the problem of indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera. The system utilizes the depth information provided by the camera for micronavigation, which involves sensing and avoiding obstacles in the immediate environment. The INVys system proposes a novel auto-adaptive double thresholding (AADT) method to detect obstacles, calculate their distance, and provide feedback to the user to avoid them. AADT has been evaluated and compared to baseline and auto-adaptive thresholding (AAT) methods using four criteria: accuracy, precision, robustness, and execution time. The results indicate that AADT excels in accuracy, precision, and robustness, making it a suitable method for obstacle detection and avoidance in the context of indoor navigation for persons with visual impairment. In addition to micronavigation, the INVys system utilizes the color information provided by the camera for macro-navigation, which involves recognizing and following navigational markers called optical glyphs. The system uses an automatic glyph binarization method to recognize the glyphs and evaluates them using two criteria: accuracy and execution time. The results indicate that the proposed method is accurate and efficient in recognizing the optical glyphs, making it suitable for use as a navigational marker in indoor environments. Furthermore, the study also provides a correlation between the size of the glyphs, the distance of the recognized glyphs, the tilt condition of the recognized glyphs, and the accuracy of glyph recognition. These correlations define the minimum glyph size that can be practically used for indoor navigation for persons with visual impairment. Overall, this research presents a promising solution for indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera and proposing novel methods for obstacle detection and avoidance and for recognizing navigational markers.

Read full abstract
  • Jurnal Nasional Teknik Elektro dan Teknologi Informasi
  • Nov 28, 2023
  • Widyawan + 2
Open Access
Cite
Save

Path planning method for Camellia oleifera forest trenching operation based on human-robot collaboration

In order to realize the independent and safe operation of trenching robot in Camellia oleifera forest, operation path planning is the key link. Aiming at the problem of trenching operation path planning in complex dynamic Camellia oleifera forest environment, a trenching operation path planning method of Camellia oleifera forest based on human-robot collaboration was proposed. In order to meet the requirements of trenching operation in Camellia oleifera forest, the ring trenching operation path was planned around Camellia oleifera tree; Aiming at the unknown obstacles in the dynamic Camellia oleifera forest environment, the digital twin technology was used to map the dynamic Camellia oleifera forest environment into the twin space in real time, assist the operator to recognize the dynamic Camellia oleifera forest environment information and issue decision instructions. According to the man-machine collaborative decision-making instructions and dynamic environmental information, the twin environment model of Camellia oleifera forest was updated in real-time, and the collision detection method combining virtual and real was adopted to avoid collision and re-plan the trenching operation path. The experimental results show that the repetition rate of the trenching operation path of the Camellia oleifera forest planned by this method is reduced by 1.64 percentage points, the slope standard deviation is reduced by 40.365 %, and the operation path is more stable; In addition, the re-planning of the operation path of the trenching robot based on digital twin under human-robot cooperation prevents collision with the dynamic obstacles of the Camellia oleifera forest, which provides a new idea for the intelligentization of the trenching operation of the Camellia oleifera forest.

Read full abstract
  • Computers and Electronics in Agriculture
  • Nov 25, 2023
  • Jing Xu + 8
Cite
Save

Research on Obstacle Detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar.

Due to the complexity of the ocean environment, an autonomous underwater vehicle (AUV) is disturbed by obstacles when performing tasks. Therefore, the research on underwater obstacle detection and avoidance is particularly important. Based on the images collected by a forward-looking sonar on an AUV, this article proposes an obstacle detection and avoidance algorithm. First, a deep learning-based obstacle candidate area detection algorithm is developed. This algorithm uses the You Only Look Once (YOLO) v3 network to determine obstacle candidate areas in a sonar image. Then, in the determined obstacle candidate areas, the obstacle detection algorithm based on the improved threshold segmentation algorithm is used to detect obstacles accurately. Finally, using the obstacle detection results obtained from the sonar images, an obstacle avoidance algorithm based on deep reinforcement learning (DRL) is developed to plan a reasonable obstacle avoidance path of an AUV. Experimental results show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment.

Read full abstract
  • IEEE Transactions on Neural Networks and Learning Systems
  • Nov 1, 2023
  • Xiang Cao + 2
Cite
Save

Collision avoidance for autonomous surface vessels using novel artificial potential fields

As the demand for transportation through waterways continues to rise, the number of vessels plying the waters has correspondingly increased. This has resulted in a greater number of accidents and collisions between ships, some of which lead to significant loss of life and financial losses. Research has shown that human error is a major factor responsible for such incidents. The maritime industry is constantly exploring newer approaches to autonomy to mitigate this issue. This study presents the use of novel Artificial Potential Fields (APFs) to perform obstacle and collision avoidance in marine environments. This study highlights the advantage of harmonic functions over traditional functions in modeling potential fields. With a modification, the method is extended to effectively avoid dynamic obstacles while adhering to COLREGs. Improved performance is observed as compared to the traditional potential fields and also against the popular velocity obstacle approach. A comprehensive statistical analysis is also performed through Monte Carlo simulations in different congested environments that emulate real traffic conditions to demonstrate robustness of the approach.

Read full abstract
  • Ocean Engineering
  • Oct 24, 2023
  • Aditya Kailas Jadhav + 2
Open Access
Cite
Save

The Crucial Relationship: Reinforcing the Role of Microbial Mats in Early Animal Life

The stem-group eumetazoans, also known as basal animals, have been present on Earth since the Neoproterozoic era, as evidenced by the fossil record of the Ediacaran Period (Xiao and Laflamme 2009, Butterfield 2011, Darroch et al. 2018). Previously, it was thought that Ediacaran microbial mats (also called biomats) were a key factor for early animals, providing food resources and stimulating motility and burrowing strategies into the sediment (Seilacher 1999, Meyer et al. 2014, Buatois et al. 2014, Tarhan et al. 2017, Scott et al. 2020, Coutret and Néraudeau 2022). Other research has suggested that animals living within modern microbial mats could have used the latter as a source of O2, and thus they were not reliant upon bottom water oxygenation (e.g., Gingras et al. (2007), Gingras et al. (2011)). This observation leads to the hypothesis that free dissolved O2 within the microbial mats could have facilitated the evolution of primitive animals in the Ediacaran oceans (Gingras et al. 2011). This is significant because the low concentration of dissolved O2 is often considered a significant environmental obstacle for complex animals (Lyons et al. 2014, Knoll and Sperling 2014, Boag 2018). On the other hand, it is frequently observed that microbial mats have the ability to trap and bind sediment, and in some cases, they can even induce mineral precipitation. Following the process of lithification, the once "soft" biofilms are transformed into biolaminated organosedimentary structures known as stromatolites (Konhauser 2009). Critically, the earliest biomineralized metazoans (e.g., Cloudina - Namacalathus) are found within biostromal carbonate reefs supported by microbialites (Hofmann and Mountjoy 2001, Penny et al. 2014; also illustrated in Fig. 1A, B: Byng Formation in the Mont Robson area (BC, Canada)). Characterized as sessile and gregarious, epibenthic filter feeders, we propose that the earliest biomineralized metazoans derived advantages from stromatolitic reefs by becoming encrusted or attached to them in shallow water environments (Fig. 1A, B: white arrows). Stromatolites are regarded as fossilized relics of microbial communities and occupied various subaqueous and shallow water environments, such as tidal flats, potentially dating back as far as 3.4 billion years ago (Gehling 1999, Walter et al. 1980). However, there is a lack of study regarding the role of stromatolites in the life of early animals. Recent field investgations, led by our group, in Cooking Lake (Canada) have demonstrated that animals are burrowing into sediments and actively exploiting the microbial mats not only for food resources, but also for oxygen (Fig. 1C-E). Other extensive Ediacaran microbialites (e.g., Fig. 1F) have been discovered in recent field studies in the Byng Formation from the Jasper area (AB, Canada). Interestingly, the earliest biomineralized metazoans were described from a similar depositional environment (Fig. 1A, B: Byng Formation in the Mont Robson area (BC, Canada). Consequently, we aim to reinterpret the role of microbial mats in early animal life by examining: 1) trace fossils associated with fossilized microbial textures; 2) modern 'soft' biofilms that produce O2 with fresh bioturbations; and 3) mineralized bioconstructions (stromatolitic biostromes and thrombolitic reef-mound carbonates from the Ediacaran period). These reinterpretations will enable us to speculate about the significance of microbial communities, such as oxygenic photosynthetic cyanobacteria, on early animal evolution.

Read full abstract
  • ARPHA Conference Abstracts
  • Oct 18, 2023
  • Baptiste Coutret + 3
Open Access
Cite
Save

Water resources contamination and health hazards by textile industry effluent and glance at treatment techniques: A review

Groundwater is very important part of life and source of survival on earth; and the clean water is one of the most important issues of the developed or developing world. In addition to other secondary problems such as solid waste and resource waste management, wastewater management is a major environmental obstacle to the development of the textile industry. Textile industry is the largest industrial sector of the world and hold very important part for the development of economy in all countries. Although the industry generates a lot of chemicals, including dyes in the form of wastewater. The textile industry is most fresh water consuming industry; simultaneously it discharges the huge load of contamination in our environment. The textile industry is the largest producer of wastewater during its different production processes. The wastewater creates several problems such as health problems, aquatic life including water pollution. The textile industry wastewater is treated by different methods such as coagulation, adsorption, membrane, and biological treatment. This paper reviews the different treatment technologies for textile wastewater along with its advantages and disadvantages. The objective of this study is to contribute a summary on the best and latest treatment techniques for textile industry effluent.

Read full abstract
  • Waste Management Bulletin
  • Oct 18, 2023
  • Aijaz Panhwar + 5
Open Access
Cite
Save

Obstacle Avoidance for Automated Guided Vehicles in Real-World Workshops Using the Grid Method and Deep Learning

An automated guided vehicle (AGV) obstacle avoidance system based on the grid method and deep learning algorithm is proposed, aiming at the complex and dynamic environment in the industrial workshop of a tobacco company. The deep learning object detection is used to detect obstacles in real-time for the AGV, and feasible paths are generated by the grid method, which ultimately finds an AGV obstacle avoidance solution in complex dynamic environments. The experimental results showed that the proposed system can effectively identify and avoid obstacles in a simulated tobacco production workshop environment, resulting in the average obstacle avoidance success rate of 98.67%. The transportation efficiency of cigarette factories is significantly improved with the proposed system, reducing the average execution time of handing tasks by 27.29%. This paper expects to provide a reliable and efficient solution for AGV obstacle avoidance in real-world industrial workshops.

Read full abstract
  • Electronics
  • Oct 17, 2023
  • Xiaogang Li + 6
Open Access
Cite
Save

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • .
  • .
  • .
  • 10
  • 1
  • 2
  • 3
  • 4
  • 5

Popular topics

  • Latest Artificial Intelligence papers
  • Latest Nursing papers
  • Latest Psychology Research papers
  • Latest Sociology Research papers
  • Latest Business Research papers
  • Latest Marketing Research papers
  • Latest Social Research papers
  • Latest Education Research papers
  • Latest Accounting Research papers
  • Latest Mental Health papers
  • Latest Economics papers
  • Latest Education Research papers
  • Latest Climate Change Research papers
  • Latest Mathematics Research papers

Most cited papers

  • Most cited Artificial Intelligence papers
  • Most cited Nursing papers
  • Most cited Psychology Research papers
  • Most cited Sociology Research papers
  • Most cited Business Research papers
  • Most cited Marketing Research papers
  • Most cited Social Research papers
  • Most cited Education Research papers
  • Most cited Accounting Research papers
  • Most cited Mental Health papers
  • Most cited Economics papers
  • Most cited Education Research papers
  • Most cited Climate Change Research papers
  • Most cited Mathematics Research papers

Latest papers from journals

  • Scientific Reports latest papers
  • PLOS ONE latest papers
  • Journal of Clinical Oncology latest papers
  • Nature Communications latest papers
  • BMC Geriatrics latest papers
  • Science of The Total Environment latest papers
  • Medical Physics latest papers
  • Cureus latest papers
  • Cancer Research latest papers
  • Chemosphere latest papers
  • International Journal of Advanced Research in Science latest papers
  • Communication and Technology latest papers

Latest papers from institutions

  • Latest research from French National Centre for Scientific Research
  • Latest research from Chinese Academy of Sciences
  • Latest research from Harvard University
  • Latest research from University of Toronto
  • Latest research from University of Michigan
  • Latest research from University College London
  • Latest research from Stanford University
  • Latest research from The University of Tokyo
  • Latest research from Johns Hopkins University
  • Latest research from University of Washington
  • Latest research from University of Oxford
  • Latest research from University of Cambridge

Popular Collections

  • Research on Reduced Inequalities
  • Research on No Poverty
  • Research on Gender Equality
  • Research on Peace Justice & Strong Institutions
  • Research on Affordable & Clean Energy
  • Research on Quality Education
  • Research on Clean Water & Sanitation
  • Research on COVID-19
  • Research on Monkeypox
  • Research on Medical Specialties
  • Research on Climate Justice
Discovery logo
FacebookTwitterLinkedinInstagram

Download the FREE App

  • Play store Link
  • App store Link
  • Scan QR code to download FREE App

    Scan to download FREE App

  • Google PlayApp Store
FacebookTwitterTwitterInstagram
  • Universities & Institutions
  • Publishers
  • R Discovery PrimeNew
  • Ask R Discovery
  • Blog
  • Accessibility
  • Topics
  • Journals
  • Open Access Papers
  • Year-wise Publications
  • Recently published papers
  • Pre prints
  • Questions
  • FAQs
  • Contact us
Lead the way for us

Your insights are needed to transform us into a better research content provider for researchers.

Share your feedback here.

FacebookTwitterLinkedinInstagram

Copyright 2024 Cactus Communications. All rights reserved.

Privacy PolicyCookies PolicyTerms of UseCareers