Hongxiao Wang¹, Hongshen Liu¹, Dingsen Zhang^1,*, Ziye Zhang¹, Yonghui Yue¹, Jie Chen²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1987-2001, 2025, DOI:10.32604/cmc.2025.060384 - 16 April 2025

Abstract Despite its immense potential, the application of digital twin technology in real industrial scenarios still faces numerous challenges. This study focuses on industrial assembly lines in sectors such as microelectronics, pharmaceuticals, and food packaging, where precision and speed are paramount, applying digital twin technology to the robotic assembly process. The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments. Based on this system, a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed, More >

Zhaohui An, Changyong Li^*, Yong Han, Mengru Niu

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1347-1366, 2025, DOI:10.32604/cmc.2025.059037 - 26 March 2025

Abstract This paper introduces an Improved Bidirectional Jump Point Search (I-BJPS) algorithm to address the challenges of the traditional Jump Point Search (JPS) in mobile robot path planning. These challenges include excessive node expansions, frequent path inflexion points, slower search times, and a high number of jump points in complex environments with large areas and dense obstacles. Firstly, we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time. We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized. Secondly, we… More >

Ghulam E Mustafa Abro^1,*, Ayman M Abdallah^1,2, Faizan Zahid³, Saleem Ahmed⁴

Intelligent Automation & Soft Computing, Vol.40, pp. 99-123, 2025, DOI:10.32604/iasc.2025.060364 - 23 January 2025

Abstract This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles (UAVs) and swarm robotics, with a specific emphasis on optimisation strategies, path planning, and formation control. The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications. The following are included: sensor-based platforms that facilitate effective obstacle avoidance, cluster-based hierarchical path planning for efficient navigation, and adaptive hybrid controllers for dynamic environments. The review emphasises the substantial contribution of optimisation techniques, including Max-Min Ant Colony Optimisation (MMACO), to the improvement of convergence… More >

Yameng Yin¹, Lieping Zhang^2,*, Xiaoxu Shi¹, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2769-2790, 2024, DOI:10.32604/cmc.2024.056791 - 18 November 2024

Abstract By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value… More >

Xinmeng Zha¹, Huilin Ren^1,*, Yi Xiong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011371

Abstract Additive manufacturing of continuous fiber composites is an emerging field that enables the tunable mechanical performance of composite structure by flexibly controlling the spatial layout of continuous fibers. Transverse isotropic strengthening is advantageous property of continuous fiber, which is favorable to align with the principal stress orientation. However, the accuracy and efficiency of traditional methods for calculating principal stress field are unguaranteed due to the inherent complexity and variability of geometries, material properties, and operational conditions in additive manufacturing. Therefore, a machine learning-assisted path planning method is proposed to robustly and efficiently generate the continuous… More >

Huilin Ren^1,2, David W. Rosen², Yi Xiong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010920

Abstract The advancement of continuous fiber-reinforced polymer additive manufacturing (CFRP-AM) enables the fabrication of structures with complex geometries and superior properties. However, current design methodologies consider toolpath design and structure optimization as separate stages, with toolpath design typically serving as a post-processing step after structure optimization. This sequential methodology limits the full exploitation of fiber reinforced polymer composites (FRPC) capabilities, particularly in achieving optimal structural integrity and manufacturability. In this paper, a manufacturing-oriented method is proposed for designing continuous FRPC structures, in which the structural layout and continuous fiber toolpaths are simultaneously optimized. The integrated design… More >

Juanling Liang¹, Wenguang Luo^1,2,*, Yongxin Qin¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1009-1027, 2024, DOI:10.32604/cmc.2024.055883 - 15 October 2024

Abstract An improved RRT* algorithm, referred to as the AGP-RRT* algorithm, is proposed to address the problems of poor directionality, long generated paths, and slow convergence speed in multi-axis robotic arm path planning. First, an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency. Second, a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to… More >

Mustafa Mohammed Alhassow^1,*, Oguz Ata², Dogu Cagdas Atilla¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 749-771, 2024, DOI:10.32604/cmc.2024.055592 - 15 October 2024

Abstract This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots.… More >

Shijie Yang¹, Jiateng Yuan¹, Zhipeng Zhang¹, Zhibo Chen^1,2, Hanchao Zhang⁴, Xiaohui Cui^1,2,3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1529-1563, 2024, DOI:10.32604/cmc.2024.055402 - 15 October 2024

Abstract In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive… More >

Shih-Lin Lin^*, Bo-Chen Lin

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456 - 12 September 2024

Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >