The rise of intelligent autonomous systems, especially in robotics and autonomous agents, has created a critical need for robust communication middleware that can ensure real-time processing of extensive sensor data. Current robotics middleware like Robot Operating System (ROS) 2 faces challenges with nondeterminism and high communication latency when dealing with large data across multiple subscribers on a multi-core compute platform. To address these issues, we present High-Performance Robotic Middleware (HPRM), built on top of the deterministic coordination language Lingua Franca (LF). HPRM employs optimizations including an in-memory object store for efficient zero-copy transfer of large payloads, adaptive serialization to minimize serialization overhead, and an eager protocol with real-time sockets to reduce handshake latency. Benchmarks show HPRM achieves up to 173x lower latency than ROS2 when broadcasting large messages to multiple nodes. We then demonstrate the benefits of HPRM by integrating it with the CARLA simulator and running reinforcement learning agents along with object detection workloads. In the CARLA autonomous driving application, HPRM attains 91.1% lower latency than ROS2. The deterministic coordination semantics of HPRM, combined with its optimized IPC mechanisms, enable efficient and predictable real-time communication for intelligent autonomous systems.

翻译：智能自主系统，尤其是在机器人学和自主智能体领域的兴起，对能够确保海量传感器数据实时处理的鲁棒通信中间件产生了迫切需求。现有的机器人中间件，如机器人操作系统（ROS）2，在多核计算平台上处理跨多个订阅者的大数据时，面临着非确定性和高通信延迟的挑战。为解决这些问题，我们提出了基于确定性协调语言 Lingua Franca（LF）构建的高性能机器人中间件（HPRM）。HPRM采用了一系列优化措施，包括用于高效零拷贝传输大负载的内存对象存储、最小化序列化开销的自适应序列化，以及采用实时套接字的急切协议以降低握手延迟。基准测试表明，在向多个节点广播大消息时，HPRM的延迟比ROS2低高达173倍。随后，我们通过将HPRM与CARLA模拟器集成，并运行强化学习智能体及目标检测工作负载，展示了HPRM的优势。在CARLA自动驾驶应用中，HPRM的延迟比ROS2低91.1%。HPRM的确定性协调语义，结合其优化的进程间通信机制，为智能自主系统提供了高效且可预测的实时通信能力。