无人机在干扰下自适应避障与轨迹规划
Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning
- 用贝叶斯主动推断融合专家示范与信号反馈
- 在线推断干扰源并动态调整飞行路径
- 适合对抗性环境下的智能无人机系统
本文提出一种面向对抗性干扰环境下无人机的分层轨迹规划框架。基于贝叶斯主动推断，该方法结合专家生成的示范与概率生成建模，编码高层符号规划、低层运动策略及无线信号反馈。部署时，无人机可在线推断以预测干扰、定位干扰源并相应调整轨迹，无需预先知晓干扰源位置。仿真结果表明，该方法达到接近专家水平的性能，显著降低通信干扰与任务成本，优于无模型强化学习基线，在动态环境中保持强泛化能力。
原文摘要 · Abstract (English)
This paper proposes a hierarchical trajectory planning framework for UAVs operating under adversarial jamming conditions. Leveraging Bayesian Active Inference, the approach combines expert-generated demonstrations with probabilistic generative modeling to encode high-level symbolic planning, low-level motion policies, and wireless signal feedback. During deployment, the UAV performs online inference to anticipate interference, localize jammers, and adapt its trajectory accordingly, without prior knowledge of jammer locations. Simulation results demonstrate that the proposed method achieves near-expert performance, significantly reducing communication interference and mission cost compared to model-free reinforcement learning baselines, while maintaining robust generalization in dynamic environments.
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