针对复杂环境下的空基回收任务规划问题，建立了考虑回收时间窗口的任务规划问题模型，采用分层框架削弱任务分配与航迹规划之间的耦合特性。首先，设计了基于剪枝概率路径图（P-PRM）与Dubins曲线的航迹预规划方法，分析无人机的可回收区间并融入任务规划流程；其次，根据空基回收问题特点设计分布式粒子群算法（DPSO）进行回收任务分配；最后，针对航迹时空约束与避障约束，基于四种同伦航迹变形方法建立航迹库，在CT空间中分析航迹可行性，以二分法优化同伦参数快速搜索最优航迹。一体规划与重规划结果证明了所提出任务规划方法的有效性，相比于集中式的粒子群算法与遗传算法，分布式粒子群算法具有搜索效率高、收敛快速的优点；相比一般航迹规划方法，剪枝概率路径图的规划速度更快且仍能保持较短的航迹长度；同伦变形方法可以在航迹规划层面满足避障与期望航时约束。
For the task planning problem of aerial recovery in complex environments, a task planning model incorporating re-covery time windows has been established, and a hierarchical framework is adopted to mitigate the coupling characteristics between task assignment and trajectory planning. A trajectory pre-planning method based on Pruned Probabilistic Roadmap (P-PRM) and Dubins curves is designed to analyze UAV recoverable intervals and integrate them into the task planning process. Subsequently, a Distributed Particle Swarm Optimization (DPSO) algorithm is?implemented for recovery task assignment, taking into account the characteristics of aerial?recovery mission. Furthermore, to satisfy both spatiotemporal and obstacle avoidance constraints, a trajectory library is?constructed utilizing four distinct homotopy-based trajectory deformation methods. The trajectory feasibility is analyzed in?Configuration-Time (CT) space, and optimal homotopy parameters are efficiently determined through binary search optimization. Experimental validation demonstrates the effectiveness of the proposed integrated planning and replanning methodology. In comparison with centralized Particle Swarm Optimization and Genetic Algorithm approaches, DPSO demonstrates superior performance in search efficiency and convergence rate. Relative to conventional trajectory planning methods in large-scale scenarios, P-PRM exhibits enhanced planning speed while maintaining compact trajectory lengths. The implemented homotopy deformation method effectively satisfies both obstacle avoidance and expected flight time constraints within the trajectory planning framework.
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