{"title":"Distributed Formation Control for Heterogeneous Robot Systems Based on Competitive Mechanism","authors":"Zhenghui Cui, Xiaoyi Gu, Ning Tan","doi":"10.1049/cit2.70081","DOIUrl":"https://doi.org/10.1049/cit2.70081","url":null,"abstract":"<p>This paper presents an adaptive formation control method for a heterogeneous robot swarm, utilising a multilevel formation task tree to model various types of formation tasks and a single-state distributed k-winner-take-all (S-DKWTA) algorithm to address the MRTA problem. In addition, we propose an enhanced load reassignment algorithm to resolve conflicts when using S-DKWTA. The S-DKWTA algorithm demonstrates the capability to manage multiple objectives and dynamically select leaders in real-time, thereby optimising formation efficiency and reducing energy consumption. The proposed approach integrates an enhanced artificial potential field (APF) to govern the motion of heterogeneous robot systems which encompasses both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), thereby achieving collision and obstacle avoidance. Simulations employing UGVs and UAVs swarm to achieve formation movement demonstrate the efficacy of this approach. The amalgamation of S-DKWTA and improved APF ensures stable and adaptable formation control, underscoring its potential for diverse multirobot applications.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"190-204"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coevolutionary Neural Dynamics With Learnable Parameters for Nonconvex Optimisation","authors":"Yipiao Chen, Wenbin Du, Huichao Cao, Long Jin","doi":"10.1049/cit2.70074","DOIUrl":"10.1049/cit2.70074","url":null,"abstract":"<p>Nonconvex optimisation plays a crucial role in science and industry. However, existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems, lacking robustness in noise scenarios. To address these limitations, we aim to develop a robust, efficient and globally convergent solver for nonconvex optimisation. This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics (PLND) model with the global search capability of the coevolutionary mechanism. We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters (CNDLP) model. The gradient information is used to find the optimal solution more effectively, and neural dynamics models have robustness, which ensures that the influence of noise can be effectively suppressed in the calculation process. Theoretical analyses show the global convergence and robustness of the designed CNDLP model. Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms. Benchmarks cover diverse problems, from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock. Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"111-122"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards Interpretable Face Morphing via Unsupervised Learning of Layer-Wise and Local Features","authors":"Cheng Yu, Chuan Chen, Wenmin Wang","doi":"10.1049/cit2.70088","DOIUrl":"https://doi.org/10.1049/cit2.70088","url":null,"abstract":"<p>Discovering meaningful face morphing is critical for applications in image synthesis. Traditional unsupervised methods rely on global or layer-wise representations, neglecting finer local details and thus limiting the control over specific facial attributes. In this work, we introduce an improved unsupervised approach that leverages contrastive learning and K-means clustering to learn both layer-wise and local features (LLF) in the latent space of StyleGAN. Our method segments latent representations into multiple local components across different layers, enabling fine-grained control over attributes such as hair, eyes, and mouth. Experimental results demonstrate that LLF outperforms existing methods by providing more interpretable facial transformations while preserving high image realism, offering a promising solution for enhanced unsupervised face morphing applications. The code is available at https://github.com/disanda/LLF.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"137-148"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}