Electronics

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15 pages, 2800 KiB

Open AccessArticle

A UUV Cluster Route-Planning Method for Dynamic Target Search

by Jingxiang Feng, Weicheng Xu, Jingwei Dong, Yao Yao and Zhixing Hu

Electronics 2024, 13(20), 4033; https://doi.org/10.3390/electronics13204033 - 13 Oct 2024

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Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is [...] Read more.

Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is established. Then, based on the Bayesian model and Markov decision model, the target probability distribution is periodically updated, and based on the cumulative detection probability optimal principle of the UUV cluster, the UUV cluster is guided to search the region with high detection probability preferentially. Finally, we implement the simulation experiment and compare with the random search method. The results verify that the proposed method has higher search efficiency in the cases of without prior information and with prior information. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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17 pages, 1307 KiB

Open AccessArticle

Station-Keeping Control of Stratospheric Balloons Based on Simultaneous Optimistic Optimization in Dynamic Wind

by Yuanqiao Fan, Xiaolong Deng, Xixiang Yang, Yuan Long and Fangchao Bai

Electronics 2024, 13(20), 4032; https://doi.org/10.3390/electronics13204032 - 13 Oct 2024

Viewed by 240

Abstract

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding [...] Read more.

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding horizon controller based on wind and balloon models. Two neural networks, PredRNN and ResNet, are utilized for short-term wind field forecast. Additionally, an online receding horizon controller, based on simultaneous optimistic optimization (SOO), is developed for action sequence planning and adapted to accommodate various constraints, which is especially suitable due to its gradient-free nature, high efficiency, and effectiveness in black-box function optimization. A reward function is formulated to balance power consumption and station-keeping performance. Simulations conducted across diverse positions and dates demonstrate the superior performance of the proposed method compared with traditional greedy and A* algorithms. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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14 pages, 2174 KiB

Open AccessFeature PaperArticle

An Adaptive Controller with Disturbance Observer for Underwater Vehicle Manipulator Systems

by Xinhui Zheng, Yuliang Wang, Qiyan Tian, Qifeng Zhang, Xiaohui Wang, Wenbo Xu, Guodong Wang, Xuejiao Yang and Yuze Sun

Electronics 2024, 13(19), 3938; https://doi.org/10.3390/electronics13193938 - 5 Oct 2024

Viewed by 437

Abstract

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that [...] Read more.

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that estimates the changes in the center of mass (COM) and the center of buoyancy (COB) of the vehicle, and the second part is the nonlinear disturbance observer that estimates the external disturbance and model uncertainties. To attenuate the overestimation problem, a damping term is introduced to the adaptive law. The stability of the proposed method is proven on the basis of Lyapunov theory. We develop three scenarios on the Simurv platform and illustrate the effectiveness of the proposed method with a short response time and high tracking performance. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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13 pages, 3668 KiB

Open AccessArticle

Underwater Target Detection Using Side-Scan Sonar Images Based on Upsampling and Downsampling

by Rui Tang, Yimin Chen, Jian Gao, Shaowen Hao and Hunhui He

Electronics 2024, 13(19), 3874; https://doi.org/10.3390/electronics13193874 - 30 Sep 2024

Viewed by 475

Abstract

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature [...] Read more.

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature maps, which may cause information loss for small targets and decrease performance in SSS images. To address this problem, based on the yolov8 network, we proposed a new underwater target detection model based on upsampling and downsampling. Firstly, we introduced a new general downsampling module called shallow robust feature downsampling (SRFD) and a receptive field convolution (RFCAConv) in the backbone network. Thereby multiple feature maps extracted by different downsampling techniques can be fused to create a more robust feature map with a complementary set of features. Additionally, an ultra-lightweight and efficient dynamic upsampling module (Dysample) is introduced to improve the accuracy of the feature pyramid network (FPN) in fusing different levels of features. On the underwater shipwreck dataset, our improved model’s mAP50 increased by 4.4% compared to the baseline model. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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14 pages, 13034 KiB

Open AccessArticle

Learning Underwater Intervention Skills Based on Dynamic Movement Primitives

by Xuejiao Yang, Yunxiu Zhang, Rongrong Li, Xinhui Zheng and Qifeng Zhang

Electronics 2024, 13(19), 3860; https://doi.org/10.3390/electronics13193860 - 29 Sep 2024

Viewed by 358

Abstract

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases [...] Read more.

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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13 pages, 685 KiB

Open AccessArticle

Research on Multiple AUVs Task Allocation with Energy Constraints in Underwater Search Environment

by Hailin Wang, Yiping Li, Shuo Li and Gaopeng Xu

Electronics 2024, 13(19), 3852; https://doi.org/10.3390/electronics13193852 - 28 Sep 2024

Viewed by 494

Abstract

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task [...] Read more.

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task allocation for multiple AUVs, with a particular focus on strategies that optimize navigation time with energy consumption constraints. By conceptualizing the multiple AUVs task allocation issue as a Capacitated Vehicle Routing Problem (CVRP) and addressing it using the SCIP solver, this study seeks to identify effective task allocation strategies that enhance the operational efficiency and minimize the mission duration in energy-restricted underwater settings. The findings of this research provide valuable insights into efficient task allocation under energy constraints, providing useful theoretical implications and practical guidance for optimizing task planning and energy management in multiple AUVs systems. These contributions are demonstrated through the improved solution quality and computational efficiency. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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12 pages, 4318 KiB

Open AccessArticle

Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator

by Jiawei Zhang, Chengchao Bai, Jifeng Guo, Zhengai Cheng and Ying Chen

Electronics 2024, 13(18), 3785; https://doi.org/10.3390/electronics13183785 - 23 Sep 2024

Viewed by 470

Abstract

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes [...] Read more.

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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10 pages, 994 KiB

Open AccessArticle

Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets with ACD-NSGA-II Algorithm

by Hong Zhang, Kunzhong Miao, Huangzhi Yu and Yifeng Niu

Electronics 2024, 13(18), 3609; https://doi.org/10.3390/electronics13183609 - 11 Sep 2024

Viewed by 338

Abstract

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the [...] Read more.

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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16 pages, 8241 KiB

Open AccessArticle

Research on Space Operation Control of Air Float Satellite Simulator Based on Constraints Aware Particle Filtering-Nonlinear Model Predictive Control

by Lingfeng Xu, Danhe Chen, Chuangge Wang and Wenhe Liao

Electronics 2024, 13(17), 3571; https://doi.org/10.3390/electronics13173571 - 8 Sep 2024

Viewed by 565

Abstract

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed [...] Read more.

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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