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 >

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 >

Yann-Horng Lin¹, Wen-Jer Chang^1,*, Yi-Chen Lee^2,*, Muhammad Shamrooz Aslam³, Cheung-Chieh Ku⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2231-2262, 2025, DOI:10.32604/cmes.2025.067464 - 31 August 2025

Abstract An Interval Type-2 (IT-2) fuzzy controller design approach is proposed in this research to simultaneously achieve multiple control objectives in Nonlinear Multi-Agent Systems (NMASs), including formation, containment, and collision avoidance. However, inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance. Based on the IT-2 Takagi-Sugeno Fuzzy Model (T-SFM), the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties. Unlike existing control methods for NMASs, the Formation and Containment (F-and-C) control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM… More >

Guojun Chen^*, Danguo Cheng, Wei Chen, Xue Yang, Tiezheng Guo

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3721-3741, 2024, DOI:10.32604/cmc.2024.047325 - 26 March 2024

Abstract With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to… 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

A. Sivaranjani^1,*, B. Vinod²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1135-1150, 2023, DOI:10.32604/iasc.2023.028126 - 06 June 2022

Abstract Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a… More >