Song Xu^1,2,*, Liang Xuan^1,2, Yifeng Li^1,2, Qiang Zhang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070472 - 09 December 2025

Abstract The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts. In the industrial robot production scenarios, the 6D pose estimation of industrial parts mainly faces two challenges: one is the loss of information and interference caused by occlusion and stacking in the sorting scenario, the other is the difficulty of feature extraction due to the weak texture of industrial parts. To address the above problems, this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts, namely CB-PVNet. On the… More >

Wei Liu^1,*, Ruiyang Wang¹, Guangwei Liu²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070328 - 09 December 2025

Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… More >

Longfei Gao^*, Weidong Wang, Dieyun Ke

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068873 - 10 November 2025

Abstract At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development. To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments, this paper proposes an Attention-Enhanced Dueling Deep Q-Network (AD-Dueling DQN), which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework. A multi-objective reward function, centered on energy efficiency, is designed to comprehensively consider path length, terrain slope, motion smoothness, and obstacle avoidance, enabling optimal low-energy trajectory generation in 3D space from the… More >

Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract

Miah A. Robinson, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.7, pp. 615-632, 2025, DOI:10.32604/jai.2025.073535 - 01 December 2025

Abstract Ensuring the safety of Artificial Intelligence (AI) is essential for providing dependable services, especially in various sectors such as the military, education, healthcare, and automotive industries. A highly effective method to boost the precision and performance of an AI agent involves multi-configuration training, followed by thorough evaluation in a specific setting to gauge performance outcomes. This research thoroughly investigates the design of three AI agents, each configured with a different number of hidden units. The first agent is equipped with 128 hidden units, the second with 256, and the third with 512, all utilizing the… More >

Junyu Ge¹, Yifan Wang¹, Hong Li^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-3, 2025, DOI:10.32604/icces.2025.011446

Abstract The advancement of untethered and adaptive robotic systems necessitates the development of compact, efficient, and silent pneumatic power sources [1,2]. Traditional pneumatic actuation relies on bulky compressors or external gas reservoirs, limiting their practical applications in mobile and autonomous systems [3,4]. This work presents a novel electrochemical pneumatic battery (EPB) that exploits electrochemical driven gas generation to achieve controlled and energy-efficient pneumatic actuation, offering a viable alternative to conventional air supply methods. The EPB operates through an electrochemical redox mechanism based on a zinc-oxygen battery [5–7], enabling reversible gas storage and controlled pressure modulation. This… More >

Joon Yau Leong^1,*, Vignesh Prasad², Carlos J. Perez Kerkvliet¹, Andrew A. Wagner³, Richard E. Link⁴, Mihir S. Shah¹

Canadian Journal of Urology, Vol.32, No.5, pp. 469-475, 2025, DOI:10.32604/cju.2025.066348 - 30 October 2025

Abstract In recent years, the introduction of the Da Vinci Single Port (SP) robotic platform has opened new doors for the treatment of localized renal masses. This technology, particularly when utilized via a regionalized retroperitoneal (RP) approach, offers several distinct advantages that may improve patient recovery. These advantages include easier access to both anterior and posterior renal tumors, avoidance of the peritoneal cavity with complicating adhesions, and simplified supine positioning, potentially reducing the risk of musculoskeletal or nerve injuries. Yet, the learning curve for RP surgery remains steep due to the unfamiliarity of many surgeons with More >

Dimindra Karki^*, Ghizlane Yaakoubi, Beth Edelblute, Ahmed Aboumohamed^*

Canadian Journal of Urology, Vol.32, No.5, pp. 501-507, 2025, DOI:10.32604/cju.2025.063294 - 30 October 2025

Abstract Background: Epithelioid angiomyolipoma (EAML) is an uncommon renal tumor variant with histologic and radiologic features that can mimic renal cell carcinoma (RCC) on imaging due to the paucity of fat compared to the classic AML. EAML may exhibit aggressive behavior, including local invasion, recurrence, and distant metastases to the liver, lungs, and lymph nodes. Although recent reports suggest that up to one-third of EAML cases may behave malignantly, variability in diagnostic criteria and limited case series contribute to uncertainty regarding its true clinical course. Case Description: This case report describes a 19-year-old female presenting with an… More >

Abubakar Elsafi^1,*, Arafat Abdulgader Mohammed Elhag², Lubna A. Gabralla³, Ali Ahmed⁴, Ashraf Osman Ibrahim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 621-647, 2025, DOI:10.32604/cmes.2025.072726 - 30 October 2025

Abstract Energy efficiency stands as an essential factor when implementing deep reinforcement learning (DRL) policies for robotic control systems. Standard algorithms, including Deep Deterministic Policy Gradient (DDPG), primarily optimize task rewards but at the cost of excessively high energy consumption, making them impractical for real-world robotic systems. To address this limitation, we propose Physics-Informed DDPG (PI-DDPG), which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies. The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor (), enabling policies that balance reward maximization with energy savings. Our motivation is to overcome the… More >

Hsiao-Chun Han¹, Der-Chen Huang^1,*, Chin-Ling Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 67-126, 2025, DOI:10.32604/cmes.2025.070944 - 30 October 2025

Abstract This study presents a systematic review of applications of artificial intelligence (abbreviated as AI) and blockchain in supply chain provenance traceability and legal forensics cover five sectors: integrated circuits (abbreviated as ICs), pharmaceuticals, electric vehicles (abbreviated as EVs), drones (abbreviated as UAVs), and robotics—in response to rising trade tensions and geopolitical conflicts, which have heightened concerns over product origin fraud and information security. While previous literature often focuses on single-industry contexts or isolated technologies, this review comprehensively surveys these sectors and categorizes 116 peer-reviewed studies by application domain, technical architecture, and functional objective. Special attention More >