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<p>In post-disaster search and rescue scenarios, robotic path planning must operate in unpredictable, dynamic environments where conventional coverage path planning (CPP) algorithms often struggle to adapt. To address this challenge, we propose an intelligent path planning algorithm called PERM-QN (Q-learning with priority experience replay and memory network), designed for energy-aware, complete area coverage in uncertain terrains. PERM-QN integrates a dynamic weight reward function, priority experience replay, and a memory network to enable efficient, comprehensive exploration in complex, obstacle-laden environments. The dynamic weight reward function adaptively balances coverage, path length, and energy consumption across different phases of operation, and the priority experience replay mechanism accelerates learning convergence by focusing on high-value past experiences. Finally, the memory network expedites route planning in regions with similar terrain, reducing redundant exploration. Experiments in simulated post-disaster environments of varying complexity demonstrate that PERM-QN achieves more efficient and comprehensive exploration than traditional methods while maintaining robust performance. These findings highlight PERM-QN as an effective path planning solution for robotic search in complex, dynamic environments.</p>

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An energy aware Q-learning framework for comprehensive coverage path planning in unknown complex environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Yao Xue,
  • Chee Keong Tan,
  • Wai Peng Wong

摘要

In post-disaster search and rescue scenarios, robotic path planning must operate in unpredictable, dynamic environments where conventional coverage path planning (CPP) algorithms often struggle to adapt. To address this challenge, we propose an intelligent path planning algorithm called PERM-QN (Q-learning with priority experience replay and memory network), designed for energy-aware, complete area coverage in uncertain terrains. PERM-QN integrates a dynamic weight reward function, priority experience replay, and a memory network to enable efficient, comprehensive exploration in complex, obstacle-laden environments. The dynamic weight reward function adaptively balances coverage, path length, and energy consumption across different phases of operation, and the priority experience replay mechanism accelerates learning convergence by focusing on high-value past experiences. Finally, the memory network expedites route planning in regions with similar terrain, reducing redundant exploration. Experiments in simulated post-disaster environments of varying complexity demonstrate that PERM-QN achieves more efficient and comprehensive exploration than traditional methods while maintaining robust performance. These findings highlight PERM-QN as an effective path planning solution for robotic search in complex, dynamic environments.