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Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.

This article focuses on target search in dynamic environments using multiple solar-powered unmanned aerial vehicles (SUAVs) with the probability map method. First, the energy model of the SUAV and an external solar environment model are established. Second, the Gaussian mixture model (GMM) is used to extract the prior probability distribution of the target to obtain multiple search areas composed of two-dimensional Gaussian distributions. Then, dynamic task allocation of the SUAVs based on the environment is performed utilizing the GMM information. Next, the SUAVs search for the target according to the assignment and plan a path with the energy model and probability established before, and the probability map is updated in real time according to the detection result of the SUAVs and the velocity vector field of the environment. In this study, two probability map update methods, namely the equivalent area iteration (EAI) and probability estimated diffusion (PED) methods, are proposed for the situations of precise and imprecise environmental information, respectively. Finally, comparative simulations are carried out for these two different situations to prove the improvement obtained using SUAVs. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the cue congruency effect of auditory stimuli during visual search in dynamic environments. Twenty-eight participants were recruited to conduct a visual search experiment. The experiment applied auditory stimuli to understand whether they could facilitate visual search in different types of background. Additionally, target location and target orientation were manipulated to clarify their influences on visual search. Target location was related to horizontal visual search and target orientation was associated with visual search for an inverted target. The results regarding dynamic backgrounds reported that target-congruent auditory stimuli could speed up the visual search time. In addition, the cue congruency effect of auditory stimuli was critical for the center of the visual display but declined for the edge, indicating the inhibition of horizontal visual search behavior. Moreover, few improvements accompanying auditory stimuli were provided for the visual detection of non-inverted and inverted targets. The findings of this study suggested developing multisensory interaction with head-mounted displays, such as augmented reality glasses, in real life. [ABSTRACT FROM AUTHOR]

This paper proposes a new navigation control scheme for a three-wheel OMnidirectional Robot (OMR), taking into account the dynamic constraints with the aim of collision avoidance in dynamic environments. The proposed control strategy is inherently a multi-modal adaptive-Reinforcing model-based controller (MAR-MPC), which can be regarded as a judicious synergy of multi-modal MPC (MMPC) and Q-Learning (QL) to optimal navigation. This method takes advantage of a larger convergence area, compared to MMPC and MPC, and presents near-optimal MPC performance. The navigation scheme utilizes an online stochastic observer, namely, a Kalman filter, to estimate the future state of the robot. This paper formulates a general collision avoidance navigation problem in a constrained linear convex cone structure to make it real-time implementable. Furthermore, the proposed multi-modal path planning algorithm reduces the required prediction horizon and consequently affects computational cost. To evaluate the performance of the proposed navigation strategy, two static and two dynamic environment instances are simulated, and the results are compared with four algorithms, namely, Exploring Random Tree (RRT), Potential Field (PF), MPC, and MMPC. Results indicate the superior performance of the proposed navigation method in aspects of time and distance costs and collision avoidance criterion. Moreover, the proposed algorithm provides the feasibility and stability guarantee and a larger convergence region. [ABSTRACT FROM AUTHOR]

Cooperative hunting is a typical task that reflects the intelligence level of a swarm. For the complex underwater weak information environment with obstacles, a problem description of the multi-autonomous underwater vehicle (AUV) cooperative hunting task is given, considering the influencing factors, including underwater obstacles, AUV sensing interaction range, and target escape strategy. A hybrid adaptive preference method based on improved artificial potential fields (HAP-IAPF) is proposed. Then the strategies of obstacle avoidance and hunting are designed separately according to the task requirements. The adaptive weight control unit is used to adjust the preference strategy. The multi-AUV cooperative hunting in dynamic obstacle underwater environments under weakly connected conditions are achieved. In order to prove the effectiveness of the proposed algorithm, simulation results compared with the traditional artificial potential field method and the optimized artificial potential field method are given in this paper. The results show that the proposed method is robust and effective in different environments. [ABSTRACT FROM AUTHOR]

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes. [ABSTRACT FROM AUTHOR]

Dynamic environments pose a severe challenge to visual SLAM as moving objects invalidate the assumption of a static background. While recent works employ deep learning to address the challenge, they still fail to determine whether an object actually moves or not, resulting in the misguidance of object tracking and background reconstruction. Hence we design a SLAM system to simultaneously estimate trajectory and construct object-level dense 3D semantic maps in dynamic environments. Synergizing deep learning-based object detection, we leverage geometric constraints by using optical flow and the relationship between objects to identify those moving but predefined static objects. To construct more precise 3D semantic maps, our method employs an unsupervised algorithm to segment 3D point cloud generated by depth data into meaningful clusters. The 3D point clusters are then synergized with semantic cues generated by deep learning to produce a more accurate 3D semantic map. We evaluate the proposed system on TUM RGB-D dataset and ICL-NUIM dataset as well as in real-world indoor environments. Qualitative and quantitative experiments show that our method outperforms state-of-the-art approaches in various dynamic scenes in terms of both accuracy and robustness. [ABSTRACT FROM AUTHOR]

This article deals with the problem of mobile robot path planning in an unknown environment that contains both static and dynamic obstacles, utilizing a reinforcement learning approach. We propose an improved Dyna- ${Q}$ algorithm, which incorporates heuristic search strategies, simulated annealing mechanism, and reactive navigation principle into ${Q}$ -learning based on the Dyna architecture. A novel action-selection strategy combining $\varepsilon $ -greedy policy with the cooling schedule control is presented, which, together with the heuristic reward function and heuristic actions, can tackle the exploration-exploitation dilemma and enhance the performance of global searching, convergence property, and learning efficiency for path planning. The proposed method is superior to the classical ${Q}$ -learning and Dyna- ${Q}$ algorithms in an unknown static environment, and it is successfully applied to an uncertain environment with multiple dynamic obstacles in simulations. Further, practical experiments are conducted by integrating MATLAB and robot operating system (ROS) on a physical robot platform, and the mobile robot manages to find a collision-free path, thus fulfilling autonomous navigation tasks in the real world. [ABSTRACT FROM AUTHOR]

Dynamism in the higher education industry (HEI) has created unprecedented complexity and uncertainty for leaders at colleges and universities across the globe. The challenges to competitive advantage and sustainability created by dynamic conditions have been exacerbated and accelerated by the COVID-19 pandemic and health crisis. HEI leaders are struggling to adapt antiquated and traditionally held methods and practices to navigate the rapid changes and survive the resulting chaos. The ability of leaders to adapt to dynamism in contemporary industry conditions is influenced by the external and internal environments; in term, these leaders' capacity for change impacts the adaptability of the institutions they serve. This study examines how senior- and mid-level leaders at small private liberal arts colleges and universities are engaging in adaptive behaviors to help their organizations better compete despite challenges. [ABSTRACT FROM AUTHOR]

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents. [ABSTRACT FROM AUTHOR]

This research introduces a new path planning method for rescue robots in a dynamic and partially known area when the robots are performing tasks in a large area. The path planning of the rescue robots that move in the dynamic area is introduced to solve the issue of unnecessary areas, which are the disadvantages of the existing D*-based algorithms. This paper proposes a method to eliminate unnecessary expanded nodes of the dynamic and partially known environment by segmenting a map with an auto-clustering algorithm, which is able to achieve a faster execution time than conventional algorithms. Furthermore, to show the effectiveness of the proposed algorithms, an expected value of re-planned nodes in the dynamic and partially known area is introduced using a probability-based approach. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Disruptive strategies have only been studied in stable environments. To overcome this limitation, this article aims to show through an in-depth study of the main actors in the yoghurt industry in Algeria that these strategies are possible in a dynamic environment. This research was carried out on the basis of an exploratory qualitative study. We have focused our efforts on two types of players, including new entrants and incumbent businesses. Our results show that the strong factors of the competitive dynamics pushed companies and industries to undertake disruptive innovation, which strategy became an indispensable element to survive and succeed for all the actors in the industry. Overall, our study indicates that the link between the competitive dynamics and disruptive innovation is not only in need of, but is deserving of, more consideration.

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed‐loop online dense RGB‐D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two‐wheel differential‐drive robot. It exhibits lightweight three‐dimensional mapping, room‐scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on‐the‐fly navigation while mapping.

Underwater Wireless Sensor Networks (UWSNs) face unique constraints due to their unstructured and dynamic underwater environment. Data gathering from these networks is crucial as energy resources are limited. In this regard, efficient routing protocols are needed to optimize energy consumption, increase the network lifetime, and enhance data delivery in these networks. In this work, we develop an Adaptive Distributed Routing Protocol for UWSNs using Deep Q-Learning (ADRP-DQL). This protocol employs the ability of reinforcement learning to dynamically learn the best routing decisions based on the network's state and action-value estimates. It allows nodes to make intelligent routing decisions, considering residual energy, depth and node degree. A Deep Q-Network (DQN) is employed as the function approximator to estimate action values and choose the optimal routing decisions. The DQN is trained using off-policy and on-policy strategies and the neural network model. Simulation results demonstrate that ADRP-DQL performs well regarding energy efficiency (EE), data delivery ratio, and network lifetime. The results highlight the proposed protocol's effectiveness and adaptability to UWSNs. The ADRP-DQL protocol contributes to intelligent routing for UWSNs, offering a promising approach to enhance performance and optimize energy utilization in these demanding environments. [ABSTRACT FROM AUTHOR]

Traditional visual Simultaneous Localization and Mapping (SLAM) methods based on point features are often limited by strong static assumptions and texture information, resulting in inaccurate camera pose estimation and object localization. To address these challenges, we present SLAM 2 , a novel semantic RGB-D SLAM system that can obtain accurate estimation of the camera pose and the 6DOF pose of other objects, resulting in complete and clean static 3D model mapping in dynamic environments. Our system makes full use of the point, line, and plane features in space to enhance the camera pose estimation accuracy. It combines the traditional geometric method with a deep learning method to detect both known and unknown dynamic objects in the scene. Moreover, our system is designed with a three-mode mapping method, including dense, semi-dense, and sparse, where the mode can be selected according to the needs of different tasks. This makes our visual SLAM system applicable to diverse application areas. Evaluation in the TUM RGB-D and Bonn RGB-D datasets demonstrates that our SLAM system achieves the most advanced localization accuracy and the cleanest static 3D mapping of the scene in dynamic environments, compared to state-of-the-art methods. Specifically, our system achieves a root mean square error (RMSE) of 0.018 m in the highly dynamic TUM w/half sequence, outperforming ORB-SLAM3 (0.231 m) and DRG-SLAM (0.025 m). In the Bonn dataset, our system demonstrates superior performance in 14 out of 18 sequences, with an average RMSE reduction of 27.3% compared to the next best method. • Semantic vSLAM fusing point, line and plane improves perception and camera pose estimation. • Dense, semi-dense and sparse modes for flexible mapping in dynamic scenes. • 6DOF pose estimation of objects for enriched semantic information in mapping. • Semantic and motion fusion enables advanced pose estimation in dynamic environments. • Depth and plane-based refinement for cleaner global static 3D point cloud maps. [ABSTRACT FROM AUTHOR]

Maintaining the sustainability of an organization on the market and their performance in a competitive market is very difficult to achieve. In this dynamic environment, organizations decide to cooperate, becoming partners in a virtual enterprise. In order to work efficiently, the partners must collaborate and coordinate their negotiation activities. This paper proposes an agent-based collaborative platform for coordinating concurrent negotiations among organizations acting in the same industrial market. The underlying complexity is to model the dynamic environment where multi-attribute and multi-participant negotiations are racing over a set of heterogeneous resources.

It is challenging to ensure satisfying co-evolution efficiency for the multi-agents in dynamic environments since during Actor-Critic training there is a high probability of falling into local optimality, failing to adapt to the suddenly changed environment quickly. To solve this problem, this paper proposes a multi-agent adaptive co-evolution method in dynamic environments (ACE-D) based on the classical multi-agent reinforcement learning method MADDPG, which effectively realizes self-adaptive new environments and co-evolution in dynamic environments. First, an experience screening policy is introduced based on the MADDPG method to reduce the negative influence of original environment experience on exploring new environments. Then, an adaptive weighting policy is applied to the policy network, which accordingly generates benchmarks for varying environments and assigns higher weights to those policies that are more beneficial for new environments exploration, so that to save time while promoting adaptability of the agents. Finally, different types of dynamic environments with complexity at different levels are built to verify the co-evolutionary effects of the two policies separately and the ACE-D method comprehensively. The experimental results demonstrate that, compared with a range of other methods, the ACE-D method has obvious advantages helping multi-agent adapt to dynamic environments and preventing them from falling into local optima, with more than 25% improvement in stable reward and more than 23% improvement in training efficiency. The ACE-D method is valuable and commendable to promote the co-evolutionary effect of multi-agent in dynamic environments.

The integration of Connected Cruise Control (CCC) and wireless Vehicle-to-Vehicle (V2V) communication technology aims to improve driving safety and stability. To enhance CCC’s adaptability in complex traffic conditions, in-depth research into intelligent asymmetrical control design is crucial. In this paper, the intelligent CCC controller issue is investigated by jointly considering the dynamic network-induced delays and target vehicle speeds. In particular, a deep reinforcement learning (DRL)-based controller design method is introduced utilizing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. In order to generate intelligent asymmetrical control strategies, the quadratic reward function, determined by control inputs and vehicle state errors acquired through interaction with the traffic environment, is maximized by the training that involves both actor and critic networks. In order to counteract performance degradation due to dynamic platoon factors, the impact of dynamic target vehicle speeds and previous control strategies is incorporated into the definitions of Markov Decision Process (MDP), CCC problem formulation, and vehicle dynamics analysis. Simulation results show that our proposed intelligent asymmetrical control algorithm is well-suited for dynamic traffic scenarios with network-induced delays and outperforms existing methods.

Recent studies in warehouse logistics have highlighted the importance of multi-robot collaboration in collective transport scenarios, where multiple robots work together to lift and transport bulky and heavy items. However, limited attention has been given to task allocation in such scenarios, particularly when dealing with continuously arriving tasks and time constraints. In this paper, we propose a decentralized auction-based method to address this challenge. Our approach involves robots predicting the task choices of their peers, estimating the values and partnerships associated with multi-robot tasks, and ultimately determining their task choices and collaboration partners through an auction process. A unique "suggestion" mechanism is introduced to the auction process to mitigate the decision bias caused by the leader–follower mode inherent in typical auction-based methods. Additionally, an available time update mechanism is designed to prevent the accumulation of schedule deviations during the robots' operation process. Through extensive simulations, we demonstrate the superior performance and computational efficiency of the proposed algorithm compared to both the Agent-Based Sequential Greedy Algorithm and the Consensus-Based Time Table Algorithm, in both dynamic and static scenarios. • Proposes an auction-based method for task allocation in collective transport. • It manages continuously arriving tasks with time window constraints. • Introduces a suggestion mechanism to mitigate decision bias in leader–follower mode. • A state update mechanism accommodates unexpected schedule deviations. • Its efficacy and scalability in addressing large-scale issues is validated. [ABSTRACT FROM AUTHOR]

Existing Global Path Planning (GPP) algorithms predominantly presume planning in static environments. This assumption immensely limits their applications to Unmanned Surface Vehicles (USVs) that typically navigate in dynamic environments. To address this limitation, we present OkayPlan, a GPP algorithm capable of generating safe and short paths in dynamic scenarios at a real-time executing speed (125 Hz on a desktop-class computer). Specifically, we approach the challenge of dynamic obstacle avoidance by formulating the path planning problem as an Obstacle Kinematics Augmented Optimization Problem (OKAOP), which can be efficiently resolved through a PSO-based optimizer at a real-time speed. Meanwhile, a Dynamic Prioritized Initialization (DPI) mechanism that adaptively initializes potential solutions for the optimization problem is established to further ameliorate the solution quality. Additionally, a relaxation strategy that facilitates the autonomous tuning of OkayPlan's hyperparameters in dynamic environments is devised. Comprehensive experiments, including comparative evaluations, ablation studies, and applications to 3D physical simulation platforms, have been conducted to substantiate the efficacy of our approach. Results indicate that OkayPlan outstrips existing methods in terms of path safety, length optimality, and computational efficiency, establishing it as a potent GPP technique for dynamic environments. The video and code associated with this paper are accessible at https://github.com/XinJingHao/OkayPlan. • An obstacle kinematics augmented path planning problem is formulated. • A dynamic prioritized initialization mechanism is introduced to improve planning. • A relaxation strategy is established to facilitate hyperparameter tuning. • A real-time path planning algorithm for dynamic environments is established. [ABSTRACT FROM AUTHOR]

This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.

In recent years, with the rapid development of artificial intelligence, machine vision and other related technologies, there has been a demand for higher levels of intelligence in UAVs. There are many excellent SLAM systems available, but most of them assume that their working environment is static. When there are dynamic objects in the environment the localization and mapping accuracy of the SLAM system is reduced, and it can even cause its tracking to fail. To solve this problem. In this paper, we propose a target detection‐based SLAM algorithm for real‐time operation in crowded dynamic environments. The algorithm removes feature points of dynamic objects from key frames and then constructs a point cloud map of based on the state‐of‐the‐art SLAM system ORB‐SLAM3. Finally, the proposed method is validated and evaluated on multiple dynamic datasets and real environments. The results show that the algorithm in this paper outperforms other visual SLAM algorithms in terms of localization and map building accuracy in more highly dynamic datasets, while guaranteeing performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article. [ABSTRACT FROM AUTHOR]

The task allocation problem is an important research field in unmanned aerial vehicles (UAVs). However, most existing task allocation algorithms can form coalitions to address the resources constraints, but cannot support starting tasks at the same time, nor can cope with the new emerging tasks flexibly. To this end, we propose a novel resource-constrained task allocation method based on the performance impact algorithm (RCPIA) to support simultaneously starting tasks and provide more flexibility to reallocate the new tasks. More specifically, based on the proposed task allocation model, we firstly modify the task inclusion phase and conflict resolution phase of the baseline PI algorithm to preferentially allocate the tasks to the UAVs that can complete tasks individually. After that, to make full use of resources and further allocate remaining unassigned tasks, a two-stage coalition formation method is creatively proposed to form a coalition for the tasks that cannot be performed by a single UAV to provide enough resources. Especially, an idle time slot mechanism (ITSM) is investigated to shift the start times of tasks that can be performed by a single UAV to create a longer feasible time slot to insert the task. Thirdly, the reassignment application of the two-stage coalition method is introduced to cope with new emerging tasks. Finally, numerical simulations are constructed to illustrate the procedure of RCPIA and verify the superiority of RCPIA compared with other task allocation algorithms in efficiency and success allocation rate. [ABSTRACT FROM AUTHOR]

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neurofuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg-Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the accuracy of attitude of unmanned aerial vehicle (UAV) navigation system in dynamic environment, an attitude calculation algorithm based on acceleration correction model is proposed. First, the acceleration correction model is established to calculate the estimated non-gravitational acceleration and external non-gravitational acceleration to modify the output value of the accelerometer, which reduces the influence of non-gravitational acceleration on the attitude calculation in dynamic environment. Then, the attitude calculation model based on Kalman filter is built, attitude angle calculated by corrected acceleration and magnetometer as measurement of filtering model, and the attitude calculation algorithm based on the acceleration correction model is designed. The experimental results show that the algorithm can reduce the interference of non-gravitational acceleration to attitude calculation, which avoids attitude angle divergence of UAV navigation system in dynamic environment, and improves the accuracy and anti-interference ability of UAV navigation system in dynamic environment.

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm’s performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.

A Grid-Local Probability Road Map (PRM) method was proposed for the path planning of manipulators in dynamic environments. Based on the idea of boundary discretization, a double-grid model was built to obtain a mapping from dynamic obstacles to configuration space. The collision detection was simplified as a data indexing process to improve its efficiency. Times of collision detections were reduced by employing local programming strategies and the stratified sampling method. Moreover, the validity of sampling was increased. Taking the PUMA560 manipulator as a research object, the simulation experiments show that the time consumption of the proposed simplified collision-detection algorithm is about 14% of that of the standard one, and the stratified sampling is beneficial to the generation of probability maps compared with simple random sampling method. The simulation experiment of the static path planning shows that the proposed algorithm consumes an average of 10ms, which is superior to the comparison algorithm and has high efficiency and real-time performance. The simulation experiment of the dynamic path planning shows that the proposed algorithm consumes an average of 7ms per step, which is better than the comparison algorithm. The proposed algorithm can adjust the global path in real time to avoid obstacles as the environment changes. The algorithm mentioned has been proved to be efficient.

The development of sensing technologies has enabled the estimation of human thermal sensation, which helps efficient heating, ventilation, and air conditioning control. Recently, machine learning techniques using physiological and environmental sensors have been proposed. However, in most existing literature, the personal thermal sensation is estimated using only opportunistic data as features, and evaluations are conducted in a static environment. In real situations, we are exposed to a dynamic environment, owing to human mobility and changes in airflow. Therefore, it is necessary to estimate personal thermal sensation in a dynamic surrounding environment. In this study, we propose TSVNet, a deep-learning-based method reflecting time-series changes to address the dynamic environment. We collected data for a total of 123 days, which included the dynamic environment data from 21 subjects, for the evaluation. The results indicate that our method improves F1-score by 5.8% compared with a baseline method. We also design a data balancing method for regression problems on imbalanced datasets, including time-series data. In addition, the result of the lookback time evaluation shows that the use of physiological information in the previous 10 minutes improves the performance of the method.

Due to the moist environment and inevitable movement, efficient wound closure and healing of vulnerable joint skin remains a great challenge. Herein, a poly(γ‐glutamic acid)‐crosslinked amino‐functionalized PEGylated poly(glycerol sebacate) (γ‐PGA/PEGS‐NH2) adhesive hydrogel is reported. PEGS‐NH2 and γ‐PGA not only forms covalent amide bonds with biological tissue surfaces to achieve strong moist adhesion but also establishes a stable chemically crosslinked network in bulk hydrogels to resist deformation. Furthermore, bioinspired gallic acid‐modified chitosan (CS‐GA) is introduced to enhance moist adhesion via multiple hydrogen bonds and establish a dynamic physically crosslinked network to dissipate energy. Consequently, this adhesive hydrogel strongly adheres to moist biological tissue, showing an adhesion six times higher than that of fibrin glue and comparable to that of strong cyanoacrylate glue. Moreover, benefiting from high mechanical resilience and effective energy dissipation, 200 cycles of loading–unloading mechanical tests conducted under an adhesive state and a full‐thickness rat skin incision model applied on a dynamic nape further confirmed the desirable dynamic tissue adhesion and wound healing performance. Combining the above ideal features with their good injectability and shape‐adaptability to complex contours, such adhesive hydrogels are demonstrated to be promising candidates for joint wound closure and healing in moist and dynamic physiological environment. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the visual SLAM algorithm in dynamic scenes is susceptible to motion feature points, resulting in low accuracy and poor robustness of pose estimation, this paper proposed a RGB-D visual SLAM algorithm based on dynamic region culling. Firstly, identify the feature points belonging to the moving objects with the help of semantic information, and detected whether the feature points remain stationary at this point used multi-view geometry with the help of the depth information of the camera. Use feature points extracted from static objects and static feature points derived from movable objects to fine-tune the camera pose estimation to achieve accurate and robust operation of the system in dynamic scenes. Finally, it used the dynamic indoor scenes in the TUM dataset for experimental verification. Experiments show that the proposed algorithm can effectively improve the accuracy of the camera′s pose estimation in the indoor dynamic environment, realize the map update in the dynamic environment, and improve the accuracy of the environment construction while enhancing the robustness of the system. [ABSTRACT FROM AUTHOR]

The essence of this paper is to study and characterise the performance of Indian Regional Navigation Satellite System (IRNSS), also known as NavIC (Navigation with Indian Constellation), in actual dynamic environment by conducting field trials on military aircraft. The dynamic accuracy of IRNSS receiver in terms of position, velocity and altitude has not yet been tested on any aircraft system i.e. commercial or military applications. These field trials and performance evaluation will help in on-field assessment of IRNSS receiver performance with availability of five, six and seven IRNSS satellites. During conduct of trials, IRNSS receiver and IRNSS antenna were placed inside the aircraft. Results analysis of 18 hours flying data depicts position availability of 99.872% with five satellites and 100% position availability with six and seven satellites. [ABSTRACT FROM AUTHOR]

For decades, both empirical and theoretical models have been proposed to explain the patterns and mechanisms of collective decision-making (CDM). The most-studied CDM scenario is the best-of-n problem in a static environment. However, natural environments are typically dynamic. In dynamic environments, the visual occlusions produced by other members of a large-scale group are also common. Hence, some agents of a group are less informed than others, and their state uncertainties increase. This paper develops a new model referred to as the generalized Ising model with dynamic confidence (GIM-C) to reduce the state uncertainty induced by visual occlusions. The proposed model first estimates the expected rewards of possible actions with dynamic confidence weighting. It then gives the probability of choosing each action based on the generalized Ising model with an external field defined by the last stage's results. Numerical simulations demonstrate that GIM-C shares the key feature of social cohesion with previous CDM models. Furthermore, in order to illustrate the efficiency of the proposed GIM-C, the collecting foraging task is considered, where a large-scale group of agents is required to obtain rewards with the presence of a dynamic predator and visual occlusions. The good performance of GIM-C in the collecting foraging task demonstrates that dynamic confidence weighting is efficient in reducing the state uncertainty introduced by visual occlusions. The proposed GIM-C also demonstrates the importance of enhancing the influence of informed agents in CDM problems in a dynamic environment with visual occlusions. [ABSTRACT FROM AUTHOR]

Energy-efficient path planning is essential for the autonomous underwater vehicle (AUV)-based ocean exploration. Existing static environment-based AUV path planners do not work well in dynamic ocean environments. A novel onboard sensing system-based AUV path planning strategy is proposed, and it is suitable for a regional dynamic environment to improve the energy utilization efficiency of an AUV working in a small-scale and dynamic mission area. Firstly, unlike the existing methods, the onboard sensing system including horizontal acoustic doppler current profile and detecting sonar is used to obtain environmental information, and the probabilistic multiple hypothesis tracker and Kalman filter are employed to carry out multi-step prediction of the environment. After that, the differential evolution algorithm is introduced as the optimizer, and a novel prediction-based path evaluator is designed to evaluate the fitness of possible paths. Besides, a novel prediction-based online re-planning strategy is designed, which is beneficial to reduce the impact of forecast error and the planning is thus closed-loop. Finally, multiple simulation experiments are designed to verify the effectiveness and superiority of the path planner, and the results show that the proposed planning strategy can reduce the AUV energy consumption by at least 4.6% compared with static environment-based planners. [ABSTRACT FROM AUTHOR]

This paper considers the task reassignment problem for distributed multiple Unmanned Aerial Vehicle (multi-UAV) systems in dynamic environment. For a dynamic reassignment problem in a multi-UAV system, the task information may be subject to different dynamic events, and many existing task allocation algorithms require much computation and communication resource to achieve a feasible solution. Hence, this paper proposes a distributed method to cope with dynamic events that occur online during the execution of original schedules. First, a distributed framework for determining the processing strategy according to the types of dynamic events is introduced. Second, a partial reassignment algorithm (PRA) is proposed to support the framework and an incremental subteam formation mechanism and a partial releasing mechanism are developed to release the computation and communication burden. Furthermore, a modified inclusion phase to maximize assignment (MIP-MA) is also proposed in PRA to maximize the number of task allocations. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution with less data exchanges and runtime. [ABSTRACT FROM AUTHOR]