异构无人集群的协同规划是支撑未来空天地一体化行动、智能安防及无人作战等复杂场景下的重要技术。这类
场景中的任务往往以时序约束、协同关系、与动态目标相耦合的形式给出，且动态目标受自身上层意图驱动并具备反应
机动能力，仅依赖短时轨迹外推难以支撑异构集群协同规划方案的可行性与任务执行的高效性，为在线决策、协同分工
和实时规划带来显著挑战。针对这一问题，文章提出了面向动态时序任务的异构集群意图感知协同规划方法，利用环境
语义与目标行为信息，对动态目标实现上层意图推理和长时轨迹预测，并将预测结果显式嵌入异构集群的协同规划过程，
形成了“意图预测-协同决策-动态调整”的在线闭环决策机制。实验结果表明，该方法在动态任务更新与目标行为模式
变化的情况下，能够保证任务推进符合逻辑约束的同时，保持有效性和决策质量，平均任务执行时间均值降低22.9%，
方差降低32.2%，克服了传统规划方法在复杂动态场景下执行效率低、动态调整弱的问题，为面向动态时序任务实现实
时、高效的异构无人集群协同规划提供了新的技术支撑。
Cooperative planning for heterogeneous multi-agents system is an important enabling technology for complex scenarios
such as future space-air-ground integrated operations, intelligent security, and unmanned combat. In such scenarios, tasks are often
specified with temporal constraints, cooperative requirements, and coupling with dynamic targets. Moreover, dynamic targets are
driven by their own high-level intention and may exhibit reactive maneuvers. Therefore, relying solely on short-term trajectory
prediction is hard to ensure the feasibility of cooperative planning solutions and the efficiency of task execution for heterogeneous
system, which poses significant challenges to online decision-making, cooperative allocation, and real-time planning. To address
these problems, the paper proposes an intention-aware cooperative planning method for heterogeneous multi-agents system with
dynamic temporal tasks. By leveraging environmental semantics and target behavioral information, the method performs high-level
intention estimation and long-horizon trajectory prediction for dynamic targets, and explicitly incorporates the prediction results
into the cooperative planning process of heterogeneous system. In this way, an online closed-loop decision-making mechanism
integrating “intention prediction, cooperative decision-making, and online adjustment” is established. Simulation results show that,
under dynamic task updates and changes in target behavioral patterns, the proposed method can maintain the logical consistency of
task progression and improve decision quality. Compared with conventional planning methods, the proposed method reduces the
mean task execution time by 22.9% and variance by 32.3%, overcoming the limitations of low execution efficiency and weak
dynamic adaptability in complex dynamic scenarios. This work provides new technical support for real-time and efficient
cooperative planning for heterogeneous multi-agents system with dynamic temporal tasks.
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