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Abstract: Abstract The path planning problem of complex wild environment with multiple elements still poses challenges. This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning. The modeling process of wild environment map is designed. Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles, redundant nodes and twisting paths. A new weighted cost function is designed to achieve different planning modes. Furthermore, the improved dynamic window approach (DWA) is designed to avoid local optimality and improve time efficiency compared to traditional DWA. For the necessary path re-planning of wild environment, the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements. The improved fusion algorithm effectively solves problems and consumes less time, and the simulation results verify the effectiveness of improved algorithms above. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2731-2
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Abstract: Abstract To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances, the event-triggered fixed-time consensus protocol is proposed. First, the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters. Second, an event-triggered mechanism is presented to solve the problem of frequent communication between agents, and triggered condition based on state information is given for each follower. It is available to save communication resources, and the Zeno behaviors are excluded. Then, the delay and switching topologies of the system are also discussed. Next, the system stabilization is analyzed by Lyapunov stability theory. Finally, simulation results demonstrate the validity of the presented method. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2695-2
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Abstract: Abstract Multi-agent reinforcement learning has recently been applied to solve pursuit problems. However, it suffers from a large number of time steps per training episode, thus always struggling to converge effectively, resulting in low rewards and an inability for agents to learn strategies. This paper proposes a deep reinforcement learning (DRL) training method that employs an ensemble segmented multi-reward function design approach to address the convergence problem mentioned before. The ensemble reward function combines the advantages of two reward functions, which enhances the training effect of agents in long episode. Then, we eliminate the non-monotonic behavior in reward function introduced by the trigonometric functions in the traditional 2D polar coordinates observation representation. Experimental results demonstrate that this method outperforms the traditional single reward function mechanism in the pursuit scenario by enhancing agents’ policy scores of the task. These ideas offer a solution to the convergence challenges faced by DRL models in long episode pursuit problems, leading to an improved model training performance. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2713-4
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Abstract: Abstract The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles and the complex interactions between agents’ actions. These factors contribute to a tendency for the solution to converge slowly, and in some cases, diverge altogether. In addressing this issue, this paper introduces a novel approach utilizing a double dueling deep Q-network (D3QN), tailored for dynamic multi-agent environments. A novel reward function based on multi-agent positional constraints is designed, and a training strategy based on incremental learning is performed to achieve collaborative path planning of multiple agents. Moreover, the greedy and Boltzmann probability selection policy is introduced for action selection and avoiding convergence to local extremum. To match radar and image sensors, a convolutional neural network - long short-term memory (CNN-LSTM) architecture is constructed to extract the feature of multi-source measurement as the input of the D3QN. The algorithm’s efficacy and reliability are validated in a simulated environment, utilizing robot operating system and Gazebo. The simulation results show that the proposed algorithm provides a real-time solution for path planning tasks in dynamic scenarios. In terms of the average success rate and accuracy, the proposed method is superior to other deep learning algorithms, and the convergence speed is also improved. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2732-1
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Abstract: Abstract This paper aims to study the leader-following consensus of linear multi-agent systems on undirected graphs. Specifically, we construct an adaptive event-based protocol that can be implemented in a fully distributed way by using only local relative information. This protocol is also resource-friendly as it will be updated only when the agent violates the designed event-triggering function. A sufficient condition is proposed for the leader-following consensus of linear multi-agent systems based on the Lyapunov approach, and the Zeno-behavior is excluded. Finally, two numerical examples are provided to illustrate the effectiveness of the theoretical results. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2718-z
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Abstract: Abstract To solve the synchronization and tracking problems, a cooperative control scheme is proposed for a class of higher-order multi-input and multi-output (MIMO) nonlinear multi-agent systems (MASs) subjected to uncertainties and external disturbances. First, coupled relationships among Laplace matrix, leader-following adjacency matrix and consensus error are analyzed based on undirected graph. Furthermore, nonlinear disturbance observers (NDOs) are designed to estimate compounded disturbances in MASs, and a distributed cooperative anti-disturbance control protocol is proposed for high-order MIMO nonlinear MASs based on the outputs of NDOs and dynamic surface control approach. Finally, the feasibility and effectiveness of the proposed scheme are proven based on Lyapunov stability theory and simulation experiments. PubDate: 2024-08-01 DOI: 10.1007/s12204-023-2673-0
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Abstract: Abstract Double-integrator multi-agent systems (MASs) might not achieve dynamical consensus, even if only partial agents suffer from self-sensing function failures (SSFFs). SSFFs might be asynchronous in real engineering application. The existing fault-tolerant dynamical consensus protocol suitable for synchronous SSFFs cannot be directly used to tackle fault-tolerant dynamical consensus of double-integrator MASs with partial agents subject to asynchronous SSFFs. Motivated by these facts, this paper explores a new fault-tolerant dynamical consensus protocol suitable for asynchronous SSFFs. First, multi-hop communication together with the idea of treating asynchronous SSFFs as multiple piecewise synchronous SSFFs is used for recovering the connectivity of network topology among all normal agents. Second, a fault-tolerant dynamical consensus protocol is designed for double-integrator MASs by utilizing the history information of an agent subject to SSFF for computing its own state information at the instants when its minimum-hop normal neighbor set changes. Then, it is theoretically proved that if the strategy of network topology connectivity recovery and the fault-tolerant dynamical consensus protocol with proper time-varying gains are used simultaneously, double-integrator MASs with all normal agents and all agents subject to SSFFs can reach dynamical consensus. Finally, comparison numerical simulations are given to illustrate the effectiveness of the theoretical results. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2716-1
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Abstract: Abstract This paper studies the time-varying formation-containment tracking control problems for unmanned aerial vehicle (UAV) swarm systems with switching topologies and a non-cooperative target, where the UAV swarm systems consist of one tracking-leader, several formation-leaders, and followers. The formation-leaders are required to accomplish a predefined time-varying formation and track the desired trajectory of the tracking-leader, and the states of the followers should converge to the convex hull spanned by those of the formation-leaders. First, a formation-containment tracking protocol is proposed with the neighboring relative information, and the feasibility condition for formation-containment tracking and the algebraic Riccati equation are given. Then, the stability of the control system with the designed control protocol is proved by constructing a reasonable Lyapunov function. Finally, the simulation examples are applied to verify the effectiveness of the theoretical results. The simulation results show that both the formation tracking error and the containment error are convergent, so the system can complete the formation containment tracking control well. In the actual battlefield, combat UAVs need to chase and attack hostile UAVs, but sometimes when multiple UAVs work together for military interception, formationcontainment tracking control will occur. PubDate: 2024-08-01 DOI: 10.1007/s12204-024-2728-x
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Abstract: Abstract This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints, deck motion, measurement noises, and unknown disturbances. The iterative model predictive control (MPC) strategy with constraints is proposed for automatic landing control of the aircraft. First, the long short-term memory (LSTM) neural network is used to calculate the adaptive reference trajectories of the aircraft. Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator. Second, an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory. Moreover, some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC. Finally, the simulation results of F/A-18A aircraft show that compared with the conventional MPC, the presented MPC strategy improves the computational efficiency by nearly 56% and satisfies the control performance requirements of carrier landing. PubDate: 2024-08-01 DOI: 10.1007/s12204-023-2690-z
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Abstract: Abstract Medical image segmentation plays a crucial role in facilitating clinical diagnosis and treatment, yet it poses numerous challenges due to variations in object appearances and sizes with indistinct boundaries. This paper introduces the MHSAttResDU-Net architecture, a novel approach to automatic medical image segmentation. Drawing inspiration from the double U-Net, multi-head self-attention (MHSA) model, and residual connections, the proposed model is trained on images pre-processed by the innovative ranking-based color constancy approach (RCC). The MHSAttResDU-Net includes the integration of RCC to control model complexity and enhance generalization across diverse lighting conditions. Additionally, the incorporation of the sparse salient region pooling (SSRP) unit in the encoder-decoder blocks reduces the dimension of feature maps, capturing essential local and global channel descriptors without introducing learnable parameters. MHSA gates are strategically employed in both down-sampling and up-sampling paths, allowing the recollection of additional relevant dimensional data. This effectively addresses dissimilar feature representations, minimizing unfocused noise and artifacts while reducing computational costs. Furthermore, Leaky ReLU-based residual connections between the encoder and decoder enhance the model’s capability to recognize complex shapes and structures, ensuring improved gradient flow and faster convergence. Experimental results demonstrate the superiority of the MHSAttResDU-Net architecture across diverse datasets, including COVID-19, ISIC 2018, CVC-ClinicDB, and the 2018 Data Science Bowl. The model achieves state-of-the-art performance metrics, including an accuracy of 99%, representing a promising advancement in automated medical image analysis with potential implications for improving patient care and diagnostic accuracy. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2756-6
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Abstract: Abstract Traditional motor function assessment for stroke patients involves subjective scoring by rehabilitation physicians, a process that is time-consuming, expensive, and subject to variability. By utilizing sensors (markers) and machine learning algorithms, digital assessment systems offer the potential for more objective clinical decision support. Nevertheless, these algorithms often rely on feature extraction, which opens up opportunities for improving diagnostic accuracy and reliability. Therefore, this study proposed a novel assessment approach based on markless-sensing technology and deep learning algorithms, which can perform contactless data measurement and nonfeature-based digital assessment. Specifically, the movement data of stroke patients were collected via the Microsoft Kinect V2 with a customized motion tracking system. The raw dataset was then processed by the Savitzky-Golay filter and long short-term memory-attention-based assessment model. A total of 25 volunteers (15 stroke patients and 10 healthy subjects) were recruited for experimental validation by conducting the commonly used clinical scale (wolf motor function test-functional ability scores, WMFT-FAS). The experiments showed that the proposed novel digital assessment system could achieve favorable results: an average accuracy of 91.7%, average precision of 87.4%, average recall of 87.3%, and average F1-score of 87.3%. In summary, the proposed system can provide objective and reasonably accurate assessment outcomes, which might have the potential to offer essential clinical information for rehabilitation physicians to make reasonable clinical intervention plans. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2754-8
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Abstract: Abstract This paper presents a costmap A* guided soft actor-critic (CMA-SAC) path planning method to optimize the navigation performance of robots in long-distance and complex environments. Initially, a costmap is constructed to calculate the cost for approaching obstacles. With the costmap, the improved A* algorithm effectively avoids the paths being too close to obstacles. Subsequently, a local path planner based on deep reinforcement learning is constructed to directly generate control commands for the robot. Lastly, a tightly coupled strategy of global and local path planning is employed, where the results of global path planning are incorporated as part of the input to the deep neural network and integrated into the reward function of reinforcement learning (RL). Simulation experiments indicate that the CMA-SAC method outperforms deep deterministic policy gradient and SAC algorithms in terms of learning speed and stability during training. And in the test tasks, the CMA-SAC method performs better than other RL-based methods in navigation efficiency and has better dynamic obstacle avoidance performance than dynamic window approach. The proposed method has a success rate of 95.8% in the maze environment and the highest success rate in the long-distance and dynamic environment, demonstrating the method’s ability in complex navigation tasks. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2755-7
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Abstract: Abstract Frequency-modulated continuous-wave radar enables non-contact and privacy-preserving recognition of human behavior. However, the accuracy of behavior recognition is directly influenced by the spatial relationship between human posture and the radar. To address the issue of low accuracy in behavior recognition when the human body is not directly facing the radar, a method combining local outlier factor with Doppler information is proposed for the correction of multi-classifier recognition results. Initially, information such as distance, velocity, and micro-Doppler spectrogram of the target is obtained using the fast Fourier transform and histogram of oriented gradients - support vector machine methods, followed by preliminary recognition. Subsequently, Platt scaling is employed to transform recognition results into confidence scores, and finally, the Doppler - local outlier factor method is utilized to calibrate the confidence scores, with the highest confidence classifier result considered as the recognition outcome. Experimental results demonstrate that this approach achieves an average recognition accuracy of 96.23% for comprehensive human behavior recognition in various orientations. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2580-z
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Abstract: Abstract The knee joint is structurally complex and there are numerous factors that influence knee dynamics. Therefore, it is valuable to study the effect of stride length on knee contact during walking. Moreover, it is crucial to study the mechanical properties of the knee joint for the protection of the knee joint and the mechanism of knee diseases. In this study, a healthy volunteer was invited to investigate the kinematics of the lower limb under different stride lengths by conducting motion capture experiments. Then, a complete and detailed finite element model of the knee was established, and the effect of stride length on the knee contact was studied using the finite element method, where the boundary conditions and loads were set up in accordance with the actual working conditions based on the data obtained from the motion capture experiments. When the stride length was increased by 23.08% compared with the habitual stride length, the knee flexion angle at the beginning moment of the single-legged support phase could be increased by 108.12%, the maximum von Mises stress values on the femur cartilage and meniscus were increased from 5.888 to 16.023 MPa and from 5.599 to 17.387 MPa, respectively, and the high-stress zone on the contact surface was also significantly shifted. When the stride length was reduced by 12.31% compared to the habitual stride length, the knee flexion angle at the moment of the end of the single-legged support phase was reduced by 62.22%, and the maximum von Mises stress values on the femur cartilage and meniscus were reduced from 5.362 MPa to 2.074 MPa and from 5.255 MPa to 1.986 MPa, respectively. The results of this paper indicate that when exercising and preventing or treating stride knee diseases by walking, people should choose a suitable stride for exercise according to the health condition of the knee and avoid over-pursuing a large stride to improve the exercise effect, while a smaller stride is suitable for most people. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2577-7
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Abstract: Abstract CircRNAs, widely found throughout the human bodies, play a crucial role in regulating various biological processes and are closely linked to complex human diseases. Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis, treatment, and disease prevention. However, existing models have limitations in accurately capturing similarities, handling the sparse and noise attributes of association networks, and fully leveraging bioinformatical aspects from multiple viewpoints. To address these issues, this study introduces a new non-negative matrix factorization-based framework called NMFMSN. First, we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity, respectively. Given the sparse known associations between circRNAs and diseases, we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions. Finally, we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations. Upon conducting 5-fold cross-validation and leave-one-out cross-validation, the AUC values for NMFMSN reach 0.971 2 and 0.976 8, respectively, outperforming the currently most advanced models. Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2575-9
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Abstract: Abstract In the face of the large number of people with motor function disabilities, rehabilitation robots have attracted more and more attention. In order to promote the active participation of the user’s motion intention in the assisted rehabilitation process of the robots, it is crucial to establish the human motion prediction model. In this paper, a hybrid prediction model built on long short-term memory (LSTM) neural network using surface electromyography (sEMG) is applied to predict the elbow motion of the users in advance. This model includes two sub-models: a back-propagation neural network and an LSTM network. The former extracts a preliminary prediction of the elbow motion, and the latter corrects this prediction to increase accuracy. The proposed model takes time series data as input, which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units. The offline and online tests were carried out to verify the established hybrid model. Finally, average root mean square errors of 3.52° and 4.18° were reached respectively for offline and online tests, and the correlation coefficients for both were above 0.98. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2581-y
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Abstract: Abstract The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery. And the study will be integrated with augmented reality technology to achieve three-dimensional visualization, thereby enhancing the complete resection rate of tumor and the success rate of surgery. Based on the preoperative MRI data of the patients, a 3D virtual model is reconstructed and 3D printed. A brain biomimetic model is created using gel injection molding. By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables, the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery. An orthogonal experiment is conducted on the biomimetic model to establish a predictive model, and this model is incorporated into the augmented reality navigation system. To validate the predictive model, five participants wore HoloLens2 devices, overlaying the patient’s 3D virtual model onto the physical head model. Subsequently, the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models (actual coordinates and predicted coordinates, respectively). The difference between these coordinates represents the model’s prediction error. The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from −0.678 7 mm to 0.295 7 mm and −0.4314 mm to 0.225 3mm, respectively. The relative errors for each experimental group are within 10%, demonstrating a good fit of the model. This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations. It also provides a new approach for surgeons. By combining augmented reality visualization, it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner. PubDate: 2024-07-24 DOI: 10.1007/s12204-024-2576-8
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Abstract: Abstract A distributed model predictive control (DMPC) method based on robust control barrier function (RCBF) is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment. The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance. RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements, and security constraints are achieved through a combination. Then, the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation. To ensure safe control, the optimization problem is integrated with the DMPC method. Finally, the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives. Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security. PubDate: 2024-06-24 DOI: 10.1007/s12204-024-2747-7
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Abstract: Abstract In recent years, the path planning for multi-agent technology has gradually matured, and has made breakthrough progress. The main difficulties in path planning for multi-agent are large state space, long algorithm running time, multiple optimization objectives, and asynchronous action of multiple agents. To solve the above problems, this paper first introduces the main problem of the research: multi-objective multi-agent path finding with asynchronous action, and proposes the algorithm framework of multi-objective loose synchronous (MO-LS) search. By combining A* and M*, MO-LS-A* and MO-LS-M* algorithms are respectively proposed. The completeness and optimality of the algorithm are proved, and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm, verifying that the proposed MO-LS-M* algorithm has certain advantages. PubDate: 2024-06-24 DOI: 10.1007/s12204-024-2744-x
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Abstract: Abstract The study on ship wakes of synthetic aperture radar (SAR) images holds great importance in detecting ship targets in the ocean. In this study, we focus on the issues of low quantity and insufficient diversity in ship wakes of SAR images, and propose a method of data augmentation of ship wakes in SAR images based on the improved cycle-consistent generative adversarial network (CycleGAN). The improvement measures mainly include two aspects: First, to enhance the quality of the generated images and guarantee a stable training process of the model, the least-squares loss is employed as the adversarial loss function; Second, the decoder of the generator is augmented with the convolutional block attention module (CBAM) to address the issue of missing details in the generated ship wakes of SAR images at the microscopic level. The experiment findings indicate that the improved CycleGAN model generates clearer ship wakes of SAR images, and outperforms the traditional CycleGAN models in both subjective and objective aspects. PubDate: 2024-06-24 DOI: 10.1007/s12204-024-2746-8
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