Deploying deep learning agents for autonomous navigation in unstructured environments faces critical challenges regarding safety, data scarcity, and limited computational resources. Traditional solvers often suffer from high latency, while emerging learning-based approaches struggle to ensure deterministic feasibility. To bridge the gap from embodied to embedded intelligence, we propose a self-supervised framework incorporating a differentiable hard constraint projection layer for runtime assurance. To mitigate data scarcity, we construct a Global-Guided Artificial Potential Field (G-APF), which provides dense supervision signals without manual labeling. To enforce actuator limitations and geometric constraints efficiently, we employ an adaptive neural projection layer, which iteratively rectifies the coarse network output onto the feasible manifold. Extensive benchmarks on a test set of 20,000 scenarios demonstrate an 88.75\% success rate, substantiating the enhanced operational safety. Closed-loop experiments in CARLA further validate the physical realizability of the planned paths under dynamic constraints. Furthermore, deployment verification on an NVIDIA Jetson Orin NX confirms an inference latency of 94 ms, showing real-time feasibility on resource-constrained embedded hardware. This framework offers a generalized paradigm for embedding physical laws into neural architectures, providing a viable direction for solving constrained optimization in mechatronics. Source code is available at: https://github.com/wzq-13/SSHC.git.

翻译：在无结构环境中部署深度学习智能体进行自主导航面临安全性、数据稀缺性和计算资源有限等关键挑战。传统求解器通常存在高延迟问题，而新兴的基于学习的方法难以确保确定性可行性。为弥合具身智能与嵌入式智能之间的鸿沟，我们提出了一种自监督框架，该框架集成了可微分硬约束投影层以实现运行时保障。为缓解数据稀缺问题，我们构建了全局引导人工势场（G-APF），无需人工标注即可提供密集监督信号。为高效执行执行器限制与几何约束，我们采用自适应神经投影层，将粗糙的网络输出迭代修正至可行流形。在包含20,000个场景的测试集上进行广泛基准测试，实现了88.75%的成功率，证实了操作安全性的提升。在CARLA中的闭环实验进一步验证了所规划路径在动态约束下的物理可实现性。此外，在NVIDIA Jetson Orin NX上的部署验证确认了94毫秒的推理延迟，表明其在资源受限的嵌入式硬件上具备实时可行性。该框架为将物理定律嵌入神经架构提供了通用范式，为解决机电系统中的约束优化问题提供了可行方向。源代码位于：https://github.com/wzq-13/SSHC.git。