Md. Faishal Rahaman¹, Xueyuan Li^1,*, Muhammad Amjad¹, Ibrahim Gasimov², Md. Shariful Islam², S. M. Abul Bashar³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.078206 - 26 February 2026

Abstract Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex, real-world environments, where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks. This paper presents a Distributed Formation Control and Obstacle Avoidance (DFCOA) framework for multi-unmanned ground vehicles (UGV). DFCOA integrates a virtual leader structure for global guidance, an improved A* path planning algorithm with an advanced cost function for efficient path planning, and a repulsive-force- based improved vector field histogram star(VFH*) technique for collision avoidance. The virtual leader generates a reference trajectory while enabling… More >

Shu-Yin Chiang^*, Shin-En Huang

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074528 - 10 February 2026

Abstract This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an More >

Hui Chen¹, Mohammed A. H. Ali^1,*, Bushroa Abd Razak¹, Zhenya Wang², Yusoff Nukman¹, Shikai Zhang¹, Zhiwei Huang¹, Ligang Yao³, Mohammad Alkhedher⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.065539 - 10 February 2026

Abstract Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning, inefficient detours, and limited adaptability to complex obstacle distributions. These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation. To address these challenges, this paper proposes a Wave Water Simulator (WWS) algorithm, leveraging a physically motivated wave equation to achieve inherently smooth, globally consistent path planning. In WWS, wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima, and selective corridor focusing reduces computational overhead in More >

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 >

Qinglei Zhang, Bai Hu^*, Jiyun Qin, Jianguo Duan, Ying Zhou

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1257-1273, 2025, DOI:10.32604/cmc.2025.059955 - 26 March 2025

Abstract Grasping is one of the most fundamental operations in modern robotics applications. While deep reinforcement learning (DRL) has demonstrated strong potential in robotics, there is too much emphasis on maximizing the cumulative reward in executing tasks, and the potential safety risks are often ignored. In this paper, an optimization method based on safe reinforcement learning (Safe RL) is proposed to address the robotic grasping problem under safety constraints. Specifically, considering the obstacle avoidance constraints of the system, the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process (CMDP). The Lagrange multiplier… More >

Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024

Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… 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 >

Bingzhu Wang^1,*, Xiangrui Ye^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1401-1425, 2024, DOI:10.32604/cmes.2024.047364 - 29 January 2024

Abstract A novel three-dimensional-fiber reinforced soft pneumatic actuator (3D-FRSPA) inspired by crab claw and human hand structure that can bend and deform independently in each segment is proposed. It has an omni-directional bending configuration, and the fibers twined symmetrically on both sides to improve the bending performance of FRSPA. In this paper, the static and kinematic analysis of 3D-FRSPA are carried out in detail. The effects of fiber, pneumatic chamber and segment length, and circular air chamber radius of 3D-FRSPA on the mechanical performance of the actuator are discussed, respectively. The soft mobile robot composed of More >

Xiaoli Li, Tongtong Jiao^#, Jinfeng Ma, Dongxing Duan, Shengbin Liang^#,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 595-617, 2024, DOI:10.32604/cmes.2023.029367 - 22 September 2023

Abstract In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the… More > Graphic Abstract

Chong Xu¹, Hao Zhu¹, Haotian Zhu², Jirong Wang¹, Qinghai Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2567-2591, 2023, DOI:10.32604/cmes.2023.029152 - 03 August 2023

Abstract A new and improved RRT^∗ algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother More > Graphic Abstract