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Traditional simultaneous localization and mapping (SLAM) systems are typically based on the assumption of a static environment. However, in practical applications, the presence of moving objects significantly reduces localization accuracy, limiting the system's versatility. To address the challenges of SLAM systems in dynamic environments, the academic community often employs computationally intensive methods such as deep learning, and some algorithms rely on expensive sensors (e.g. LiDAR or RGB-D cameras) to obtain depth information. These factors increase computational complexity or hardware costs, complicating practical deployment. To improve localization accuracy and adaptability of SLAM systems in dynamic scenarios while maintaining low deployment costs, this paper proposes a dynamic environment robust monocular inertial SLAM system named LDVI-SLAM. The system uses more cost-effective sensors—monocular cameras and inertial measurement unit (IMU)—along with lightweight computational methods. In LDVI-SLAM, first, the reliability of IMU data is verified. Then, using the ego-motion information provided by the IMU, along with epipolar constraint and an improved rotation-aware flow vector bound (R-FVB) constraint, dynamic feature points are eliminated. Additionally, this paper proposes a continuous tracking across interval frames method to enhance the distinction between static and dynamic feature points. Experimental results demonstrate that LDVI-SLAM performs effectively in dynamic environments and is easy to deploy. On the Visual-Inertial Odometry in Dynamic Environments dataset, experimental results show that compared to the deep learning-based DynaSLAM, this method reduces the root mean square error of absolute trajectory error by 10.3%. Moreover, in terms of speed, under the same computing power, the single-frame processing speed of this method is comparable to ORB-SLAM3 and is two orders of magnitude faster than DynaSLAM, significantly outperforming deep learning-based SLAM algorithms. Experiments on the Oxford Multimotion Dataset further prove that this method effectively avoids the risk of semantic classification errors, demonstrating better robustness and generality. [ABSTRACT FROM AUTHOR]

Colonies of ants can complete complex tasks without the need for centralised control as a result of interactions between individuals and their environment. Particularly remarkable is the process of path selection between the nest and food sources that is essential for successful foraging. We have designed a stochastic model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment, and a non-random global indirect interaction component based on pheromone signalling. Our model couples individual-based off-lattice ant simulations with an on-lattice characterisation of the pheromone diffusion. Using numerical simulations we have tested three pheromone-based model alternatives: (1) a single pheromone laid on the way toward the food source and on the way back to the nest; (2) single pheromone laid on the way toward the food source and an internal imperfect compass to navigate toward the nest; (3) two different pheromones, each used for one direction. We have studied the model behaviour in different parameter regimes and tested the ability of our simulated ants to form trails and adapt to environmental changes. The simulated ants behaviour reproduced the behaviours observed experimentally. Furthermore we tested two biological hypotheses on the impact of the quality of the food source on the dynamics. We found that increasing pheromone deposition for the richer food sources has a larger impact on the dynamics than elevation of the ant recruitment level for the richer food sources. [ABSTRACT FROM AUTHOR]

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm's ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency. [ABSTRACT FROM AUTHOR]

In the context of SLAM, the implementation of the PSPNet architecture has demonstrated efficacy in the removal of dynamic objects, which subsequently enhances the localization precision of visual SLAM systems operating within dynamic environments. Nonetheless, this methodology is characterized by suboptimal performance in real-time applications. This study introduces a real-time visual SLAM system that employs a streamlined PSPNet architecture to address the identified challenges. This system enhances real-time performance by optimizing the PSPNet network and incorporating a key frame selection module. It also utilizes semantic segmentation results to refine the homography matrix and optical flow methods to improve system accuracy. Comparative experiments performed on publicly available datasets demonstrate that the proposed system not only attains a high level of localization accuracy in dynamic environments but also exhibits superior real-time performance, thereby fulfilling practical requirements. [ABSTRACT FROM AUTHOR]

This paper presents a catadioptric omnidirectional thermal odometry (COTO) system that estimates the six degrees of freedom (DoF) pose of a camera using only omnidirectional thermal images in visually degraded, fast-motion, and dynamic environments. First, we design and fabricate a central hyperbolic catadioptric omnidirectional thermal camera that captures surrounding thermal images with 360 ° horizontal field of view (FoV), and improve the omnidirectional camera model and calibration method to obtain high-precision camera intrinsic parameter. Second, we propose the epipolar curve constraint combining with omnidirectional thermal object detection to significantly reduce the interference of moving objects on pose estimation. Third, the implemented COTO pipeline consists of photometric calibration, dynamic region removal, tracking and mapping to overcome the drawbacks of photometric inconsistency and large distortion in omnidirectional thermal images. Experiments have been conducted on a total of 17 sequences of Lab, Outdoor and Driving, amounting to more than 60,000 omnidirectional thermal images of real environments. The experimental results indicate that the proposed COTO system has excellent localization accuracy and unparalleled robustness over the current state-of-the-art methods. The average localization accuracy measured by the absolute trajectory error (ATE) is less than 15 cm from the ground truth in both Lab and Outdoor sequences. In addition, COTO was the only system with complete and successful tracking in all sequences. The system can be used as an innovative localization solution, particularly in challenging environments with changes in ambient light, rapid vehicle motion, and moving object interference, which can be a difficult problem for visual odometry to solve. [ABSTRACT FROM AUTHOR]

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm's engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is the core technology enabling mobile robots to autonomously explore and perceive the environment. However, dynamic objects in the scene significantly impact the accuracy and robustness of visual SLAM systems, limiting its applicability in real-world scenarios. Hence, we propose a real-time RGB-D visual SLAM algorithm designed for indoor dynamic scenes. Our approach includes a parallel lightweight object detection thread, which leverages the YOLOv7-tiny network to detect potential moving objects and generate 2D semantic information. Subsequently, a novel dynamic feature removal strategy is introduced in the tracking thread. This strategy integrates semantic information, geometric constraints, and feature point depth-based RANSAC to effectively mitigate the influence of dynamic features. To evaluate the effectiveness of the proposed algorithms, we conducted comparative experiments using other state-of-the-art algorithms on the TUM RGB-D dataset and Bonn RGB-D dataset, as well as in real-world dynamic scenes. The results demonstrate that the algorithm maintains excellent accuracy and robustness in dynamic environments, while also exhibiting impressive real-time performance. [ABSTRACT FROM AUTHOR]

This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarios were tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length, with a time of 2.3 s. In the second scenario, the algorithm was applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, the multi-objective algorithm was tested in a warehouse environment containing fixed, mobile, and multi-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments. [ABSTRACT FROM AUTHOR]

The growth of small and medium enterprises (SMEs) shows a positive trend and has even become a major highlight in the national economic landscape. Moreover, most of these businesses are owned by women. Examining how digital capability and financial literacy and a dynamic environment affect the success of female-owned small and medium-sized enterprises in Indonesia is the main goal of this study. Financial and digital capability are examined as mediators between performance and these factors. The study used a causal explanatory research approach. The data in this study were collected through questionnaires. This research surveyed 100 female-owned small and medium-sized enterprises in Indonesia’s food and beverage, herbal and pharmaceutical, fashion, and craft industries. Partial Least Square (PLS) analysis was utilized in this work as an analytical tool. The study reveals that financial literacy significantly enhances SMEs` performance by improving resource management. However, a dynamic environment alone does not impact performance unless supported by other competencies. Financial competence does not strengthen the effect of financial literacy, indicating it may not be sufficient for success. Conversely, digital competence is crucial, mediating the relationship between a dynamic environment and SMEs` performance. Therefore, SMEs with strong digital skills are better positioned to adapt and thrive, making digital competence key to sustained business success.

This paper considers a class of distributed online aggregative optimization problems over an undirected and connected network. It takes into account an unknown dynamic environment and some aggregation functions, which is different from the problem formulation of the existing approach, making the aggregative optimization problem more challenging. A distributed online optimization algorithm is designed for the considered problem via the mirror descent algorithm and the distributed average tracking method. In particular, the dynamic environment and the gradient are estimated by the averaged tracking methods, and then an online optimization algorithm is designed via a dynamic mirror descent method. It is shown that the dynamic regret is bounded in the order of O (T) . Finally, the effectiveness of the designed algorithm is verified by some simulations of cooperative control of a multi-robot system. [ABSTRACT FROM AUTHOR]

Modern agriculture's goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation. [ABSTRACT FROM AUTHOR]

Intrusion detection in industrial control systems (ICS) is crucial for maintaining the security of physical information systems. However, the existing models predominantly rely on black-box approaches, which exhibit limitations in result credibility and the ability to adapt to complex and dynamic environments. Consequently, this paper proposes an online updatable extended belief rule base model (O-EBRB) for intrusion detection in ICS. Firstly, an industrial intrusion detection model rooted in the extended belief rule base (EBRB) is established. This model excels in concurrently processing both quantitative and qualitative data, ensuring the reliability of its outcomes. Subsequently, a novel domain-based rule update methodology for integrating new observation data is proposed. By incorporating or merging fresh data into the original model, it enhances the model's adaptability in dynamic settings. Finally, employing the domain-based rule weight calculation approach, the model continues to effectively compute model parameters even with the continuous expansion of rules. Through extensive experimentation on two real-world industrial intrusion detection datasets, the results demonstrate the effectiveness of the proposed model in handling information and its robust performance in dynamic environments. [ABSTRACT FROM AUTHOR]

The proliferation of mobile devices and the increasing use of networked applications have generated enormous data that require real-time processing and low-latency responses. For real-time applications, Edge Computing (EC) gained a lot of attention which helps to overcome the challenges with traditional computing systems. There is a massive growth of data generated at the web edge, but its limited computing resources and boundary dynamics pose significant challenges. For proficient resource utilization and network management, Software-Defined Networks (SDN) integration with EC can provide control and programmability in edge networking management. However, the optimization of resources is a fundamental need and challenge of EC based on SDN. Besides, the utilization of SDN in EC deals with a centralized and distributed infrastructure further enables the processing of data in closer proximity to its origin and helps in the growing demand for efficient and high-performance computing systems. This study presents a systematic review of the current state-of-the-art pertaining to resource optimization in EC and its integration with SDN. The review of challenges, benefits, and various proposed methodologies for managing resources in Edge and SDN-based Edge environments are investigated. This paper offers valuable insights into the current state and future directions of Edge, SDN-based EC systems, which can assist organizations in designing and deploying more efficient resource consumption solutions [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) is pivotal for intelligent mobile robots. VSLAM systems can be used to identify scenes by obtaining massive amounts of redundant texture information from the environment. However, VSLAM faces a major challenge in dynamic low-light environments, in which the extraction of feature points is often difficult, leading to tracking failure with mobile robots. Therefore, we developed a method to improve the feature point extraction method used for VSLAM. We first used the contrast limited adaptive histogram equalization (CLAHE) method to increase the contrast in low-light images, allowing for the extraction of more feature points. Second, in order to increase the effectiveness of the extracted feature points, the redundant feature points were removed. We developed three conditions to filter the feature points. Finally, the proposed method was tested on popular datasets (e.g., TUM and OpenLORIS-Scene), and the results were compared with those of several traditional methods. The results of the experiments showed that the proposed method is feasible and highly robust in dynamic low-light environments. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot's position and create a map of its surroundings. Currently, visual SLAM algorithms typically yield precise and dependable outcomes in static environments, and many algorithms opt to filter out the feature points in dynamic regions. However, when there is an increase in the number of dynamic objects within the camera's view, this approach might result in decreased accuracy or tracking failures. Therefore, this study proposes a solution called YPL-SLAM based on ORB-SLAM2. The solution adds a target recognition and region segmentation module to determine the dynamic region, potential dynamic region, and static region; determines the state of the potential dynamic region using the RANSAC method with polar geometric constraints; and removes the dynamic feature points. It then extracts the line features of the non-dynamic region and finally performs the point–line fusion optimization process using a weighted fusion strategy, considering the image dynamic score and the number of successful feature point–line matches, thus ensuring the system's robustness and accuracy. A large number of experiments have been conducted using the publicly available TUM dataset to compare YPL-SLAM with globally leading SLAM algorithms. The results demonstrate that the new algorithm surpasses ORB-SLAM2 in terms of accuracy (with a maximum improvement of 96.1%) while also exhibiting a significantly enhanced operating speed compared to Dyna-SLAM. [ABSTRACT FROM AUTHOR]

The early applications of Visual Simultaneous Localization and Mapping (VSLAM) technology were primarily focused on static environments, relying on the static nature of the environment for map construction and localization. However, in practical applications, we often encounter various dynamic environments, such as city streets, where moving objects are present. These dynamic objects can make it challenging for robots to accurately understand their own position. This paper proposes a real-time localization and mapping method tailored for dynamic environments to effectively deal with the interference of moving objects in such settings. Firstly, depth images are clustered, and they are subdivided into sub-point clouds to obtain clearer local information. Secondly, when processing regular frames, we fully exploit the structural invariance of static sub-point clouds and their relative relationships. Among these, the concept of the sub-point cloud is introduced as novel idea in this paper. By utilizing the results computed based on sub-poses, we can effectively quantify the disparities between regular frames and reference frames. This enables us to accurately detect dynamic areas within the regular frames. Furthermore, by refining the dynamic areas of keyframes using historical observation data, the robustness of the system is further enhanced. We conducted comprehensive experimental evaluations on challenging dynamic sequences from the TUM dataset and compared our approach with state-of-the-art dynamic VSLAM systems. The experimental results demonstrate that our method significantly enhances the accuracy and robustness of pose estimation. Additionally, we validated the effectiveness of the system in dynamic environments through real-world scenario tests. [ABSTRACT FROM AUTHOR]

Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on the ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. Second, the static environment occluded by the dynamic target is repaired using the time-weighted multi-frame fusion background repair technique. Lastly, the filtered static feature points are used for feature matching and position calculation. Meanwhile, the semantic labeling information of static objects obtained based on the lightweight semantic segmentation network DeepLabV3+ is fused with the static environment information after background repair to generate dense point cloud maps containing semantic information, and the semantic dense point cloud maps are transformed into semantic octree maps using the octree spatial segmentation data structure. The localization accuracy of the visual SLAM system and the construction of the semantic maps are verified using the widely used TUM RGB-D dataset and real scene data, respectively. The experimental results show that the proposed semantic visual SLAM algorithm can effectively reduce the influence of dynamic targets on the system, and compared with other advanced algorithms, such as DynaSLAM, it has the highest performance in indoor dynamic environments in terms of localization accuracy and time consumption. In addition, semantic maps can be constructed so that the robot can better understand and adapt to the indoor dynamic environment. [ABSTRACT FROM AUTHOR]

This article examines the interconnected nature of digital marketing strategies, encompassing search engine optimization, content marketing, social media marketing, email marketing, influencer marketing, paid advertising, and data analysis and the performance of small and medium enterprises (SMEs) in Tanzania which encompasses various factors, including sales growth, market share, and market development. A total of 335 small and medium-sized enterprises (SMEs) used a systematic random selection method for data collection. The necessary data was gathered by the researcher using a structured questionnaire. Partial least squares–structural equation modeling (PLS–SEM) was used in the data analysis process. The results of the study demonstrate a notable correlation between the digital marketing strategies dimension and the SMEs Performance dimensions in Mbeya City. Additionally, the article reveals that there were no observed moderation impacts of dynamic environment on digital marketing strategies and SME performance. The article emphasizes the necessity for entrepreneurs to properly utilize digital marketing strategies. This article provides significant insights for scholars and practitioners interested in utilizing digital marketing methods to enhance the performance of SMEs in Tanzania. These insights are derived from a comprehensive synthesis of the most recent research in the field. Research for future studies was recommended.

IntroductionSimultaneous Localization and Mapping (SLAM) is a technology used in intelligent systems such as robots and autonomous vehicles. Visual SLAM has become a more popular type of SLAM due to its acceptable cost and good scalability when applied in robot positioning, navigation and other functions. However, most of the visual SLAM algorithms assume a static environment, so when they are implemented in highly dynamic scenes, problems such as tracking failure and overlapped mapping are prone to occur.MethodsTo deal with this issue, we propose ISFM-SLAM, a dynamic visual SLAM built upon the classic ORB-SLAM2, incorporating an improved instance segmentation network and enhanced feature matching. Based on YOLACT, the improved instance segmentation network applies the multi-scale residual network Res2Net as its backbone, and utilizes CIoU_Loss in the bounding box loss function, to enhance the detection accuracy of the segmentation network. To improve the matching rate and calculation efficiency of the internal feature points, we fuse ORB key points with an efficient image descriptor to replace traditional ORB feature matching of ORB-SLAM2. Moreover, the motion consistency detection algorithm based on external variance values is proposed and integrated into ISFM-SLAM, to assist the proposed SLAM systems in culling dynamic feature points more effectively.Results and discussionSimulation results on the TUM dataset show that the overall pose estimation accuracy of the ISFM-SLAM is 97% better than the ORB-SLAM2, and is superior to other mainstream and state-of-the-art dynamic SLAM systems. Further real-world experiments validate the feasibility of the proposed SLAM system in practical applications.

This paper introduces an innovative agent-based intelligent framework aimed at modelling and facilitating simultaneous and parallel negotiations among organisations operating within the same industrial sector. The challenge lies in capturing the dynamic nature of the environment, where negotiations involving multiple attributes and participants are competing for a diverse array of resources. The quest for enhanced competitiveness has prompted companies to establish specialised business units to navigate the pursuit of new partnerships, necessitating seamless interoperability. However, the current state of Enterprise Interoperability is susceptible to the disruptive influence of external factors, making it vulnerable to fragmentation. To adapt to necessary changes, continual adjustments must be made, albeit at the risk of introducing conflicts with existing concepts, potentially jeopardising interoperability with partner organisations or necessitating widespread modifications. In response, this paper proposes a collaborative framework to facilitate negotiations aimed at enhancing interoperability among organisations operating within the same industrial domain, leveraging a model-driven, cloud-based infrastructure and services. The objective of this study is to provide assistance to autonomous microgrids that lack the capacity to secure large contracts independently. Addressing interoperability challenges, the paper advocates negotiation activities as a fundamental solution. Negotiation serves as a means for all microgrids within the collaborative network to reach mutually acceptable resolutions. The paper presents a negotiation approach wherein intelligent agents assess and modify offers and counteroffers, supported by a communication protocol among these agents.

This work proposes an innovative approach to enhance the localization of unmanned aerial vehicles (UAVs) in dynamic environments. The methodology integrates a sophisticated object-tracking algorithm to augment the established simultaneous localization and mapping (ORB-SLAM) framework, utilizing only a monocular camera setup. Moving objects are detected by harnessing the power of YOLOv4, and a specialized Kalman filter is employed for tracking. The algorithm is integrated into the ORB-SLAM framework to improve UAV pose estimation by correcting the impact of moving elements and effectively removing features connected to dynamic elements from the ORB-SLAM process. Finally, the results obtained are recorded using the TUM RGB-D dataset. The results demonstrate that the proposed algorithm can effectively enhance the accuracy of pose estimation and exhibits high accuracy and robustness in real dynamic scenes. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot's LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%. [ABSTRACT FROM AUTHOR]

The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order to solve the problem of insufficient robustness in dynamic environments and difficulty in adapting to environmental changes, it is proposed to combine Euclidean distance and distance difference to improve the accuracy of approaching the target. In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm. We conducted simulation experiments and present the experimental results. The results prove that the use of fused neural network functions improved the success rate of approaching the target and improved the SAC algorithm at the same time, which improved the convergence speed, success rate, and avoidance capabilities of the algorithm. [ABSTRACT FROM AUTHOR]

The robustness of dense visual SLAM is still a challenging problem in dynamic environments. In this paper, we propose a novel keyframe-based dense visual SLAM to handle a highly dynamic environment by using an RGB-D camera. The proposed method uses cluster-based residual models and semantic cues to detect dynamic objects, resulting in motion segmentation that outperforms traditional methods. The method also employs motion-segmentation based keyframe selection strategies and frame-to-keyframe matching scheme that reduce the influence of dynamic objects, thus minimizing trajectory errors. We further filter out dynamic object influence based on motion segmentation and then employ true matches from keyframes, which are near the current keyframe, to facilitate loop closure. Finally, a pose graph is established and optimized using the g2o framework. Our experimental results demonstrate the success of our approach in handling highly dynamic sequences, as evidenced by the more robust motion segmentation results and significantly lower trajectory drift compared to several state-of-the-art dense visual odometry or SLAM methods on challenging public benchmark datasets. [ABSTRACT FROM AUTHOR]

Wound healing in movable parts, including the joints and neck, remains a critical challenge due to frequent motions and poor flexibility of dressings, which may lead to mismatching of mechanical properties and poor fitting between dressings and wounds; thus, increasing the risk of bacterial infection. This study proposes a sprayable zwitterionic antibacterial hydrogel with outstanding flexibility and desirable adhesion. This hydrogel precursor is fabricated by combining zwitterionic sulfobetaine methacrylate (SBMA) with poly(sulfobetaine methacrylate‐co‐dopamine methacrylamide)‐modified silver nanoparticles (PSBDA@AgNPs) through robust electrostatic interactions. About 150 s of exposure to UV light, the SBMA monomer polymerizes to form PSB chains entangled with PSBDA@AgNPs, transformed into a stable and adhesion PSB–PSB@Ag hydrogel at the wound site. The resulting hydrogel has adhesive strength (15–38 kPa), large tensile strain (>400%), suitable shape adaptation, and excellent mechanical resilience. Moreover, the hydrogel displays pH‐responsive behavior; the acidic microenvironment at the infected wound sites prompts the hydrogel to rapidly release AgNPs and kill bacteria. Further, the healing effect of the hydrogel is demonstrated on the rat neck skin wound, showing improved wound closing rate due to reduced inflammation and enhanced angiogenesis. Overall, the sprayable zwitterionic antibacterial hydrogel has significant potential to promote joint skin wound healing. [ABSTRACT FROM AUTHOR]

This research addresses the critical challenge of path planning for autonomous vehicles in dynamic environments, where obstacles may unpredictably change positions or emerge. The purpose of this study is to introduce and evaluate the effectiveness of the dynamic weighted A* algorithm, a novel approach that integrates dynamic obstacle awareness, steering dynamics and trajectory optimization for adaptive and real-time path planning. The algorithm's performance is rigorously assessed through comprehensive simulations, encompassing various grid resolutions and obstacle configurations. Results demonstrate the algorithm's capability to dynamically adapt to changing scenarios, providing a promising solution for autonomous vehicles navigating unpredictable environments. The study contributes valuable insights into the field of autonomous vehicle path planning, offering an algorithmic approach that balances efficiency, adaptability and real-time responsiveness. The outcomes of this research not only highlight the practicality and efficacy of the dynamic weighted A* algorithm but also contribute to advancing the discourse on path planning algorithms for autonomous vehicles in dynamic settings. These findings have implications for the development and implementation of robust navigation systems, ensuring the safe and efficient operation of autonomous vehicles in real-world scenarios. [ABSTRACT FROM AUTHOR]

Modern agriculture’s goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation.

Traditional simultaneous localization and mapping (SLAM) methodologies predominantly rely on the assumption of a static environment. This constraint limits the applicability of most visual SLAM systems in various real-world scenarios. In this paper, we introduce a real-time semantic visual SLAM algorithm tailored for complex dynamic environments (DRV-SLAM). DRV-SLAM leverages image analysis to identify potential moving objects and determine their current motion states. By dynamically adjusting the rejection of unreliable dynamic feature points based on the proportion of potential moving objects in the environment, DRV-SLAM significantly enhances the system’s localization accuracy and robustness in complex dynamic environments. Additionally, DRV-SLAM employs a dense mapping approach that combines global downsampling and targeted object data enhancement. This method effectively reduces the memory footprint of dense point cloud maps, enabling DRV-SLAM to efficiently construct large-scale dense point cloud maps in diverse scenarios. Experimental results show that in a highly dynamic environment, the DRV-SLAM algorithm shows an order of magnitude performance improvement compared to the traditional ORB-SLAM2 algorithm. The performance index of absolute trajectory error is significantly improved by more than 98%, and DRV-SLAM is currently one of the most real-time, accurate and robust systems for dynamic scenes.

This study presents a hybrid HHO-AVOA which is a novel optimization method that combines the strengths of Harris Hawks Optimization (HHO) and African Vulture Optimization Algorithm (AVOA) to address the path planning challenges encountered by differential wheeled mobile robots (DWMRs) navigating both static and dynamic environments, while accommodating kinematic constraints. By synergizing the strengths of both algorithms, the proposed hybrid method effectively mitigates the limitations of individual approaches, resulting in efficient and obstacle-avoiding navigation towards the target within reduced timeframes. To evaluate its efficiency, the proposed approach is compared against HHO and AVOA as well as other established methods which include whale optimization, grey wolf optimization and sine-cosine algorithms. Simulation results along with Monte Carlo analysis consistently demonstrate the superior performance of the hybrid method in both environments. In static scenarios, the hybrid algorithm achieves an average reduction of approximately 14% in path length and a 17% decrease in DWMR travel duration. In dynamic cases, it outperforms the rest with an average reduction of 27.6% in path length and a 27.2% decrease in travel duration. The algorithm’s low computational complexity is also exhibited via its fast convergence during path optimization which is a crucial attribute for real-time implementation, particularly in dynamically changing environments that demand quick decision-making. The superiority of the proposed hybrid method to balance the exploration and exploitation is also affirmed through a Wilcoxon rank-sum test with a 95% confidence interval.

Accurate measurement of interferometer arm-length difference is very important in the application fields such as fiber optic hydrophones. However, the interferometer is very sensitive to the environmental vibration and noise, which affects the accuracy of the interferometer measurement. A novel environmental random phase noise separation (ERPNS) technology is proposed to effectively reduce optical frequency domain reflectometer (OFDR)’s sensitivity to environmental vibration for the measurement of the fiber interferometer. A set of optical paths based on dual-laser coherent detection technology and the related ERPNS algorithm was developed to extract the environmental phase noise from the measurement data. The OFDR's sensitivity to vibration is decreased by ∼60 dB in theory and a measurement accuracy better than 0.05 mm for the static arm length difference of 74.51902 m in different dynamic environment is achieved experimentally, with 56 μm of spatial resolution in the maximum detectable distance of about 75 m.

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm’s engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions.

Construction is a hazardous industry due to the dynamic nature of the work environment. However, limited studies have investigated how the introduction of additional hazards affects construction fieldworkers' perception of safety risks. Therefore, this study aimed to investigate the influence of varying levels of hazard information on fieldworkers' safety risk perception (SRP) for the OSHA "fatal four" hazards. A sample of 181 fieldworkers rated the severity and frequency of safety incidents for five construction work scenarios. The study found that as new hazards were introduced in the work scenarios, the fieldworkers had a significant change in their perception of risks for about 71% of the accident causes. They were able to assess safety risks related to slip, trip, fall; struck-by; caught-in or -between; electrocution; stepping on an object; fall to a lower level; and trapped-in by collapsing. However, they failed to perceive the risk of increasing the number of workers in a hazardous work environment, indicating that workers focus on the severity of the occurrence of accidents and do not pay attention to the workers present on the scene. This study provides new insight into the role of varying degrees of hazard information in situational awareness and highlights the need for additional research at the intersection of worker exposure and situational awareness. Additionally, results from the study could inform the development of safety training to enhance workplace safety. [ABSTRACT FROM AUTHOR]

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots. [ABSTRACT FROM AUTHOR]

Most current research on dynamic visual Simultaneous Localization and Mapping (SLAM) systems focuses on scenes where static objects occupy most of the environment. However, in densely populated indoor environments, the movement of the crowd can lead to the loss of feature information, thereby diminishing the system’s robustness and accuracy. This paper proposes a visual SLAM algorithm for dense crowd environments based on a combination of the ORB-SLAM2 framework and RGB-D cameras. Firstly, we introduced a dedicated target detection network thread and improved the performance of the target detection network, enhancing its detection coverage in crowded environments, resulting in a 41.5% increase in average accuracy. Additionally, we found that some feature points other than humans in the detection box were mistakenly deleted. Therefore, we proposed an algorithm based on standard deviation fitting to effectively filter out the features. Finally, our system is evaluated on the TUM and Bonn RGB-D dynamic datasets and compared with ORB-SLAM2 and other state-of-the-art visual dynamic SLAM methods. The results indicate that our system’s pose estimation error is reduced by at least 93.60% and 97.11% compared to ORB-SLAM2 in high dynamic environments and the Bonn RGB-D dynamic dataset, respectively. Our method demonstrates comparable performance compared to other recent visual dynamic SLAM methods. [ABSTRACT FROM AUTHOR]

To solve the online path planning of multi-robots in dynamic environments, a novel secondary immune responses-based immune path planning algorithm (SIRIPPA) is presented. The algorithm comprises two immune stages. In the primary immune stage, the antibodies are mainly designed for obstacle avoidance and a primary immune kinetic model is designed in terms of the different impacts of obstacles on robot behaviors. The primary immune antibodies and their concentration values are mainly taken as the prior knowledge to accelerate the secondary immune response. In the secondary immune stage, aiming at the same obstacle antigens, which invade once more, the immune system quickly produces many behavior antibodies. Combining the primary immune results and secondary immune response results, the path planning performance of multi-robots is improved. The simulation experiment indicates that, in static environment tests, compared to corresponding immune planning algorithms, the SIRIPPA exhibits an average reduction of 6.22% in the global path length, a decrease of 23.00% in the average smoothness, and an average energy consumption reduction of 27.55%; the algorithm exhibits a better performance for path planning. The simulation test in a dynamic environment shows the good flexibility and stability of the SIRIPPA. Additionally, the experimental results in a real environment further support the validity of the SIRIPPA. [ABSTRACT FROM AUTHOR]

Research is carried out on the prediction of aircraft surface noise during flight with high maneuverability and high overload. The dual hybrid Reynolds-averaged Navier-Stokes/large eddy simulation (RANS/LES) calculation model based on zonal mixing and turbulent scale mixing is modified, and the new hybrid model is established to simulate complex separated flow accurately. A calculation method for simultaneously calculating aerodynamic/motion, internal/external flow, and compressible/incompressible flow is employed. The dual hybrid RANS/LES model combined with six-degree-of-freedom motion equations is used to simulate the maneuvering process of the aircraft and calculate the surface acoustic load. The calculation results reveal that the acoustic load at the inlet of the engine and tail nozzle is large. During positive overload flight, the surface noise is greater than negative overload due to the stronger turbulent fluctuation on the aircraft surface and the impact of jet noise at the tail nozzle. The contribution rate of jet noise to the overall sound pressure level (OASPL) of the fuselage gradually increased from front to rear. In the maneuvering state, the vortex motion on the aircraft surface is enhanced, and the pressure fluctuation is stronger than that in the cruise state, which makes the surface acoustic load greater than that in the cruise state. [ABSTRACT FROM AUTHOR]

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments. [ABSTRACT FROM AUTHOR]

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Autonomous underwater gliders (AUGs) are effective platforms for oceanic research and environmental monitoring. However, complex underwater environments with uncertainties could pose the risk of vehicle loss during their missions. It is therefore essential to conduct risk prediction to assist decision making for safer operations. The main limitation of current studies for AUGs is the lack of a tailored method for risk analysis considering both dynamic environments and potential functional failures of the vehicle. Hence, this study proposed a copula‐based approach for evaluating the risk of AUG loss in dynamic underwater environments. The developed copula Bayesian network (CBN) integrated copula functions into a traditional Bayesian belief network (BBN), aiming to handle nonlinear dependencies among environmental variables and inherent technical failures. Specifically, potential risk factors with causal effects were captured using the BBN. A Gaussian copula was then employed to measure correlated dependencies among identified risk factors. Furthermore, the dependence analysis and CBN inference were performed to assess the risk level of vehicle loss given various environmental observations. The effectiveness of the proposed method was demonstrated in a case study, which considered deploying a Slocum G1 Glider in a real water region. Risk mitigation measures were provided based on key findings. This study potentially contributes a tailored tool of risk prediction for AUGs in dynamic environments, which can enhance the safety performance of AUGs and assist in risk mitigation for decision makers. [ABSTRACT FROM AUTHOR]

This paper proposes an improved TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm to address the flaws of low success rate and slow training speed, when using the original TD3 algorithm in mobile robot path planning in dynamic environment. Firstly, prioritized experience replay and transfer learning are introduced to enhance the learning efficiency, where the probability of beneficial experiences being sampled in the experience pool is increased, and the pre-trained model is applied in an obstacle-free environment as the initial model for training in a dynamic environment. Secondly, dynamic delay update strategy is devised and OU noise is added to improve the success rate of path planning, where the probability of missing high-quality value estimate is reduced through changing the delay update interval dynamically, and the correlated exploration of the mobile robot inertial navigation system in the dynamic environment is temporally improved. The algorithm is tested by simulation where the Turtlebot3 robot model as a training object, the ROS melodic operating system and Gazebo simulation software as an experimental environment. Meanwhile, the result shows that the improved TD3 algorithm has a 16.6 % increase in success rate and a 23.5 % reduction in algorithm training time. A generalization experiment was designed finally, and it indicates that superior generation performance has been acquired in mobile robot path planning with continuous action spaces through the improved TD3 algorithm.

Abstract Moving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Rong Li,1 Jing Liu,2 Weidong Xia,3 Jingdong Ma1,4 1School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Administrative Office, Yuebei People’s Hospital, Shaoguan, People’s Republic of China; 3Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, USA; 4Institute for Smart Health Research, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCorrespondence: Jingdong Ma, School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China, Tel +8613098807808, Email jdma@hust.edu.cnPurpose: In the complex and rapidly changing healthcare environment, the dynamic capabilities of frontline employees (FLEs) to integrate resources and adapt to environmental changes are crucial. This study aims to investigate the relationship between FLEs’ self-efficacy, dynamic capabilities (including sensing capability and reconfiguring capability), and their impact on service performance.Methods: Data were collected from a matched sample of 123 doctors and 762 corresponding consumers from two medical aesthetic hospitals in China. SPSS and SmartPLS are used to test the proposed model.Results: The findings indicate that FLEs’ self-efficacy positively influences their service performance through the mediation of dynamic capabilities. Moreover, while the direct impact of FLEs’ sensing capabilities on service performance was found to be insignificant, it was observed that these capabilities indirectly affect service performance through reconfiguring capabilities.Conclusion: This study presents theories and arguments on the role of self-efficacy and dynamic capabilities in improving service performance. These findings contribute to a deeper understanding of how FLEs cultivate the dynamic capability of resource integration, offering valuable insights for the attainment of sustainable competitive advantages.Keywords: dynamic environment, frontline employee, self-efficacy, dynamic capability, service performance

Path planning is the most fundamental necessity for autonomous mobile robots. Traditionally, the path planning problem was solved using analytical methods, but these methods need perfect localization in the environment, a fully developed map to plan the path, and cannot deal with complex environments and emergencies. Recently, deep neural networks have been applied to solve this complex problem. This review paper discusses path-planning methods that use neural networks, including deep reinforcement learning, and its different types, such as model-free and model-based, Q-value function-based, policy-based, and actor-critic-based methods. Additionally, a dedicated section delves into the nuances and methods of robot interactions with pedestrians, exploring these dynamics in diverse environments such as sidewalks, road crossings, and indoor spaces, underscoring the importance of social compliance in robot navigation. In the end, the common challenges faced by these methods and applied solutions such as reward shaping, transfer learning, parallel simulations, etc. to optimize the solutions are discussed.