Obstacles In Environment Research Articles

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

Rural Settlement as an Agent of Agricultural Development in Brazil: Portrait and Challenges in the Context of the Brazilian Amazon

Objective: Study carried out in the Padre Josimo Tavares settlement project, located in the Brazilian Amazon, to portray the social reality, productivity and agricultural spatial distribution, as well as its implications for the preservation of the Amazon biome. Theoretical framework: The developmental measures adopted in the Amazon region, which include the creation of numerous settlement projects, did not consider the need to conserve natural resources. Currently, it is necessary to boost agricultural productivity combined with strategies that contribute to plant recovery and curb deforestation. Method: Data were collected in the field, and also spatial data were obtained, which were synthesized and analyzed using spatial, statistical and multicriteria analysis to verify the distribution and environmental suitability of agricultural activities. Results and conclusion: The settlers grow various plant products and most of them produce species strongly adapted to the region, which offers a productive advantage. Beef cattle farming is widely practiced; however, it is widely identified as the main cause of deforestation in the Amazon biome. To overcome this environmental obstacle, the recommended and discussed approaches were the implementation of agroforestry systems and zero deforestation commitment policies. Research implications: Knowing the socioeconomic and environmental reality of settlement projects is essential to identify strategies that increase, in an integrated manner, social, economic and environmental sustainability. Originality/value: Use of unpublished data obtained through field collections of the RADIS-UFV Project, the result of a partnership between the National Institute of Colonization and Agrarian Reform and the Federal University of Viçosa.

Obstacle avoidance USV in multi-static obstacle environments based on a deep reinforcement learning approach

Unmanned surface vehicles (USVs) are intelligent platforms for unmanned surface navigation based on artificial intelligence, motion control, environmental awareness, and other professional technologies. Obstacle avoidance is an important part of its autonomous navigation. Although the USV works in the water environment (e.g. monitoring and tracking, search and rescue scenarios), the dynamic and complex operating environment makes the traditional methods not suitable for solving the obstacle avoidance problem of the USV. In this paper, to address the issue of poor convergence of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of Deep Reinforcement Learning (DRL) in an unstructured environment and wave current interference, random walk policy is proposed to deposit the pre-exploration policy of the algorithm into the experience pool to accelerate the convergence of the algorithm and thus achieve USV obstacle avoidance, which can achieve collision-free navigation from any start point to a given end point in a dynamic and complex environment without offline trajectory and track point generation. We design a pre-exploration policy for the environment and a virtual simulation environment for training and testing the algorithm and give the reward function and training method. The simulation results show that our proposed algorithm is more manageable to converge than the original algorithm and can perform better in complex environments in terms of obstacle avoidance behavior, reflecting the algorithm’s feasibility and effectiveness.

Research on unmanned transfer vehicle path planning for raw grain warehousing

A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment.

Game of Drones: Multi-UAV Pursuit-Evasion Game With Online Motion Planning by Deep Reinforcement Learning.

As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as the ``swarm'' to execute missions. In this article, we investigate the multiquadcopter and target pursuit-evasion game in the obstacles environment. For high-quality simulation of the urban environment, we propose the pursuit-evasion scenario (PES) framework to create the environment with a physics engine, which enables quadcopter agents to take actions and interact with the environment. On this basis, we construct multiagent coronal bidirectionally coordinated with target prediction network (CBC-TP Net) with a vectorized extension of multiagent deep deterministic policy gradient (MADDPG) formulation to ensure the effectiveness of the damaged ``swarm'' system in pursuit-evasion mission. Unlike traditional reinforcement learning, we design a target prediction network (TP Net) innovatively in the common framework to imitate the way of human thinking: situation prediction is always before decision-making. The experiments of the pursuit-evasion game are conducted to verify the state-of-the-art performance of the proposed strategy, both in the normal and antidamaged situations.

Learners’ Experiences in an Online Learning Environment: An Analysis of the Impact of International Collaboration

This qualitative study investigates learners’ experiences in online international collaborative environments using both asynchronous and synchronous communication methods. Specifically, it focuses on the rewards, shortcomings, and difficulties learners encountered through posing critical questions about the nature of collaboration forms. Eight graduate interview participants completed two separate instructional technology courses, and an interpretive qualitative approach was used to deepen the understanding of the interview responses. The findings suggest a few key themes, primarily positive in nature, about the use of online international collaboration tools. While asynchronous communication platforms help to enhance, enrich, and expand course content, media-rich synchronous communication platforms allow learners to have a more significant sense of social presence, encouraging them to be more actively engaged in the learning environment. Synchronous communication platforms also help to enhance intercultural understanding and develop non-verbal communication skills. In addition to commentary about communication platforms, the participants expressed immense satisfaction with learning various skills (i.e., research and interpersonal skills, project planning, and digital literacy) in the international collaboration projects. Despite their success, the participants did encounter several challenges, most notably the incompatibility of their respective academic calendars. This is perhaps the most ubiquitous obstacle in the collegiate online collaborative learning environment.

Variation in the Spectral Content of UHF PD Signals Due to the Presence of Obstacles in the Measurement Environment

Variation in the Spectral Content of UHF PD Signals Due to the Presence of Obstacles in the Measurement Environment

Optimal Trajectory Planning Method for the Navigation of WIP Vehicles in Unknown Environments: Theory and Experiment.

Navigation of underactuated wheeled inverted pendulum (WIP) vehicles in unknown environments is still facing great difficulties, especially when the optimal motion is required. This article proposes an optimal trajectory planning method for the navigation of WIP vehicles in unknown environments, where various performance demands, such as security, smoothness, efficiency, etc., are all considered. First, a map-building algorithm based on the improved Rao-Blackwellized particle filter is applied for the WIP vehicle to construct the environmental map. Then, a multiobjective optimization using the genetic algorithm is performed to find an optimized path between the given start and target point with path length, path curvature, and safe distance being taken into consideration simultaneously. Moreover, on the basis of kinematical and dynamical analysis, velocity, and acceleration constraints are parameterized with a path parameter, and the minimum-time trajectory along the optimized path is further planned with a sequence of maximum acceleration and deceleration trajectories. Finally, a WIP vehicle platform based on the robot operating system is designed, and related experiments in a real obstacle environment are conducted to validate the feasibility of the proposed method.

A Low-Altitude Obstacle Avoidance Method for UAVs Based on Polyhedral Flight Corridor

UAVs flying in complex low-altitude environments often require real-time sensing to avoid environmental obstacles. In previous approaches, UAVs have usually carried out motion planning based on primitive navigation maps such as point clouds and raster maps to achieve autonomous obstacle avoidance. However, due to the huge amount of data in these raw navigation maps and the highly discrete map information, the efficiency of solving the UAV’s real-time trajectory optimization is low, making it difficult to meet the demand for efficient online motion planning. A flight corridor is a series of unobstructed continuous areas and has convex properties. The flight corridor can be used as a simple parametric representation to characterize the safe flight space in the environment, and used as the cost of the collision term in the trajectory back-end optimization for trajectory solving, which can improve the efficiency of real-time trajectory solving and ensure flight safety. Therefore, this paper focuses on the construction of safe flight corridors for UAVs and autonomous obstacle avoidance algorithms for UAVs based on safe flight corridors, based on a rotary-wing UAV platform, and proposes a polyhedral flight corridor construction algorithm and realizes autonomous obstacle avoidance for UAVs based on the constructed flight corridors.