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 >

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 >

Zeinel Momynkulov^1,2, Azhar Tursynova^1,2,*, Olzhas Olzhayev^1,2, Akhanseri Ikramov^1,2, Sayat Ibrayev¹, Batyrkhan Omarov^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 891-918, 2025, DOI:10.32604/cmes.2025.068615 - 30 October 2025

Abstract Robotic manipulators increasingly operate in complex three-dimensional workspaces where accuracy and strict limits on position, velocity, and acceleration must be satisfied. Conventional geometric planners emphasize path smoothness but often ignore dynamic feasibility, motivating control-aware trajectory generation. This study presents a novel model predictive control (MPC) framework for three-dimensional trajectory planning of robotic manipulators that integrates second-order dynamic modeling and multi-objective parameter optimization. Unlike conventional interpolation techniques such as cubic splines, B-splines, and linear interpolation, which neglect physical constraints and system dynamics, the proposed method generates dynamically feasible trajectories by directly optimizing over acceleration inputs while… More >

Nan Yang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011055

Abstract Integrating mechanical computing capabilities into robotic materials or systems enhances their intelligence in stimulus-response processes. However, current mechanical computing systems face limitations such as incomplete functionality, inflexible computational rules, challenges in implementing sequential and random logic operations, and lack of reusability. To address these issues, we propose a straightforward design method based on logical expressions to achieve more complex computational tasks. We developed soft B-shaped mechanical metamaterial units and introduced stress inputs through compression. The outputs are represented by light-shielding effects caused by unit deformation. Using this approach, we successfully implemented logic gates and their… More >

Sahbi Boubaker^1,2,*, Salim Hadj Said³, Souad Kamel¹, Habib Dimassi³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5703-5721, 2025, DOI:10.32604/cmc.2025.069047 - 23 October 2025

Abstract Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear, time-varying dynamics of human-robot interaction, limited sensor availability, and unknown external disturbances. This study proposes a novel control strategy that combines flatness-based control with two cascaded observers: a high-gain observer to estimate unmeasured joint velocities, and a nonlinear disturbance observer to reconstruct external torque disturbances in real time. These estimates are integrated into the control law to enable robust, state-feedback-based trajectory tracking. The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations, reflecting realistic rehabilitation conditions. Results confirm More >

Shuoshuo Shen^1,2, Jin Cheng^1,2,*, Zhenyu Liu², Jianrong Tan^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.011752

Abstract Motion stability assessment of industrial robots subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task [1–5]. To tackle this task, this study proposes a new reliability-based motion stability analysis method for industrial robots, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the comprehensive motion system model of industrial robots is established by integrating the control, drive, and multi-body motion models. The reliability-based stability model of industrial robots is presented considering the uncertainty of parameters. Subsequently, the fractional exponential moments are… More >