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 >

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 >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >