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<p>This study proposes an energy-aware extension of the classical A* algorithm for quadrotor path planning in static and dynamic three-dimensional environments. Unlike distance-based A*, which mainly favors geometrically short paths, the proposed formulation incorporates quadrotor-relevant energy terms, including hover power, aerodynamic drag, climb energy, and propulsion-efficiency losses, directly into the path-evaluation process. The objective is to generate collision-free trajectories that reduce estimated flight energy while preserving the simplicity and interpretability of graph-search planning. The proposed planner was evaluated over 200 randomized simulation episodes, including 100 static-obstacle scenarios and 100 mixed static–dynamic scenarios. Its performance was compared with representative baseline planners, including distance-based A*, Theta*, Kinodynamic RRT*, and, in the static case, an energy-optimized three-dimensional planner. The evaluation considered energy consumption, path length, flight time, trajectory smoothness, planning time, and scalability. The results show that the proposed Energy-based A* planner reduced energy consumption compared with the evaluated baselines. Mean energy savings ranged from 21.40% to 36.96% in static environments and from 28.94% to 41.45% in dynamic environments, with statistically significant improvements. Additional Gazebo Sim 8 validation using the X3 quadrotor model showed that the planned trajectories could be executed with low tracking errors. Overall, the proposed approach improves endurance-oriented quadrotor navigation while maintaining moderate computational cost and reliable collision-free trajectory generation.</p>

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Energy-optimized A* path planning for autonomous quadrotors in static and dynamic environments

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Nada Braiek,
  • Imen Saidi

摘要

This study proposes an energy-aware extension of the classical A* algorithm for quadrotor path planning in static and dynamic three-dimensional environments. Unlike distance-based A*, which mainly favors geometrically short paths, the proposed formulation incorporates quadrotor-relevant energy terms, including hover power, aerodynamic drag, climb energy, and propulsion-efficiency losses, directly into the path-evaluation process. The objective is to generate collision-free trajectories that reduce estimated flight energy while preserving the simplicity and interpretability of graph-search planning. The proposed planner was evaluated over 200 randomized simulation episodes, including 100 static-obstacle scenarios and 100 mixed static–dynamic scenarios. Its performance was compared with representative baseline planners, including distance-based A*, Theta*, Kinodynamic RRT*, and, in the static case, an energy-optimized three-dimensional planner. The evaluation considered energy consumption, path length, flight time, trajectory smoothness, planning time, and scalability. The results show that the proposed Energy-based A* planner reduced energy consumption compared with the evaluated baselines. Mean energy savings ranged from 21.40% to 36.96% in static environments and from 28.94% to 41.45% in dynamic environments, with statistically significant improvements. Additional Gazebo Sim 8 validation using the X3 quadrotor model showed that the planned trajectories could be executed with low tracking errors. Overall, the proposed approach improves endurance-oriented quadrotor navigation while maintaining moderate computational cost and reliable collision-free trajectory generation.