基于残差强化学习的无人机具身抗干扰导航中间件OA

Embodied intelligent navigation enables UAV autonomy in complex low-altitude scenarios,yet existing re-search often overlooks dynamic electromagnetic interference.To mitigate this,it is essential to extend navigation from the physical geometric space to the electromagnetic spectrum,achieving synergistic avoidance of obstacles and interference.To address the state-space explosion caused by coupling these domains,this paper introduces the middleware paradigm into electromagnetic embodied intelligence and proposes a UAV Embodied Anti-interference Navigation Middleware.Embed-ded between the mission planning layer and the flight control execution layer,it functions as a pluggable service module.It utilizes a RMDP(Residual Markov Decision Process)to decouple the optimization problem into a baseline heading and an anti-interference residual heading.By limiting the policy to learning local continuous corrections,this design significantly reduces decision complexity.Consequently,it ensures global convergence while effectively suppressing local electromag-netic risks.We further develop a RRL(Residual Reinforcement Learning)algorithm incorporating a predictive state encod-ing mechanism to infer wide-area interference situations from sparse observations,enabling proactive interference avoid-ance.Additionally,a collaborative training paradigm combining offline pre-training with controlled online fine-tuning is ad-opted to ensure rapid adaptation and cross-scenario generalization in unknown environments.High-fidelity simulations based on real urban maps and authentic onboard equipment specifications demonstrate that the method achieves a 93.6% task success rate with a 49.7 ms decision latency.Integration tests further verify that the architecture is upwardly compatible with heterogeneous global planners and downwardly compliant with constraints,offering significant reusability advantages.