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<p>Achieving an efficient and collision-free path remains a critical challenge for unmanned aerial vehicles (UAVs) due to their high mobility, dynamic environments, and energy constraints. This paper proposes a novel hybrid path-planning model that integrates Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to address these challenges. PSO is used to identify energy-efficient, destination-aware paths, while GWO enhances obstacle avoidance and threat adaptability in real time. The model is evaluated using metrics such as path optimality, energy consumption, robustness, time efficiency, and collision avoidance. Simulation results in MATLAB demonstrate that the proposed PSO-GWO model outperforms existing methods, producing shorter, safer, and more cost-efficient paths. Additionally, statistical validation through ANOVA confirms the model’s superiority in optimizing key performance indicators. The approach also supports improved quality of service (QoS) in UAV communication networks.</p>

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Bio-inspired optimal path planning for UAVs with obstacle avoidance and energy efficiency

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Awadhesh Dixit,
  • Sunil Kumar Singh

摘要

Achieving an efficient and collision-free path remains a critical challenge for unmanned aerial vehicles (UAVs) due to their high mobility, dynamic environments, and energy constraints. This paper proposes a novel hybrid path-planning model that integrates Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to address these challenges. PSO is used to identify energy-efficient, destination-aware paths, while GWO enhances obstacle avoidance and threat adaptability in real time. The model is evaluated using metrics such as path optimality, energy consumption, robustness, time efficiency, and collision avoidance. Simulation results in MATLAB demonstrate that the proposed PSO-GWO model outperforms existing methods, producing shorter, safer, and more cost-efficient paths. Additionally, statistical validation through ANOVA confirms the model’s superiority in optimizing key performance indicators. The approach also supports improved quality of service (QoS) in UAV communication networks.