Chang Su, Liangliang Zhao^*, Dongbing Xiang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.071319 - 09 December 2025

Abstract To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from… 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 >

Zhenpeng Jiang, Qingquan Liu^*, Ende Wang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068780 - 10 November 2025

Abstract Rapidly-exploring Random Tree (RRT) and its variants have become foundational in path-planning research, yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety. To address these challenges, we introduce HS-APF-RRT*, a novel algorithm that fuses layered sampling, an enhanced Artificial Potential Field (APF), and a dynamic neighborhood-expansion mechanism. First, the workspace is hierarchically partitioned into macro, meso, and micro sampling layers, progressively biasing random samples toward safer, lower-energy regions. Second, we augment the traditional APF by 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

Hongle Li¹, SeongKi Kim^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5571-5594, 2025, DOI:10.32604/cmc.2025.067424 - 23 October 2025

Abstract Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles, as well as the realism and immersion of virtual environments. However, traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments. This study addresses these limitations through a proposed real-time dynamic multiobjective (RDMO) path-planning algorithm based on an enhanced A* framework. The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real… More >

Thai-Viet Dang^1,*, Dinh-Manh-Cuong Tran¹, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2651-2680, 2025, DOI:10.32604/cmc.2025.067500 - 23 September 2025

Abstract Precise and robust three-dimensional object detection (3DOD) presents a promising opportunity in the field of mobile robot (MR) navigation. Monocular 3DOD techniques typically involve extending existing two-dimensional object detection (2DOD) frameworks to predict the three-dimensional bounding box (3DBB) of objects captured in 2D RGB images. However, these methods often require multiple images, making them less feasible for various real-time scenarios. To address these challenges, the emergence of agile convolutional neural networks (CNNs) capable of inferring depth from a single image opens a new avenue for investigation. The paper proposes a novel ELDE-Net network designed to… More >

Zhuoyan Xie¹, Qi Wang^1,*, Bin Kong^2,*, Shang Gao¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3013-3027, 2025, DOI:10.32604/cmc.2025.064147 - 03 July 2025

Abstract In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition… More >

Huiru Cao¹, Shaoxin Li², Xiaomin Li^3,*, Yongxin Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5575-5593, 2025, DOI:10.32604/cmc.2025.061954 - 19 May 2025

Abstract Efficient flight path design for unmanned aerial vehicles (UAVs) in urban environmental event monitoring remains a critical challenge, particularly in prioritizing high-risk zones within complex urban landscapes. Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency. To address these gaps, this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization (ACO) algorithm with an Orthogonal Jump Point Search (OJPS) algorithm. Firstly, a two-dimensional grid model is constructed to simulate urban environments, with key monitoring nodes selected based on… More >

Wenli Lei^1,2,*, Xinghao Wu^1,2, Kun Jia^1,2, Jinping Han^1,2

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5679-5698, 2025, DOI:10.32604/cmc.2025.061268 - 19 May 2025

Abstract Aiming to address the limitations of the standard Chimp Optimization Algorithm (ChOA), such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle (UAV) path planning, this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm (IChOA). First, this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints, transforming the path planning problem into an optimization problem with multiple constraints. Second, this paper enhances the diversity of the chimpanzee population by applying the Sine… More >