RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics

Intelligent robots are designed to effectively navigate dynamic and unpredictable environments laden with moving mechanical elements and objects. Such environment-induced dynamics, including moving obstacles, can readily alter the computational demand (e.g., the creation of new tasks) and the structure of workloads (e.g., precedence constraints among tasks) during runtime, thereby adversely affecting overall system performance. This challenge is amplified when multi-task inference is expected on robots operating under stringent resource and real-time constraints. To address such a challenge, we introduce RED, a systematic real-time scheduling approach designed to support multi-task deep neural network workloads in resource-limited robotic systems. It is designed to adaptively manage the Robotic Environmental Dynamics (RED) while adhering to real-time constraints. At the core of RED lies a deadline-based scheduler that employs an intermediate deadline assignment policy, effectively managing to change workloads and asynchronous inference prompted by complex, unpredictable environments. This scheduling framework also facilitates the flexible deployment of MIMONet (multi-input multi-output neural networks), which are commonly utilized in multi-tasking robotic systems to circumvent memory bottlenecks. Building on this scheduling framework, RED recognizes and leverages a unique characteristic of MIMONet: its weight-shared architecture. To further accommodate and exploit this feature, RED devises a novel and effective workload refinement and reconstruction process. This process ensures the scheduling framework's compatibility with MIMONet and maximizes efficiency. We have implemented RED on several widely used embedded and mobile platforms, including the NVIDIA Jetson Nano, TX2, Xavier, and Orin platforms. We evaluated its performance using workloads that span a broad range of settings typical in navigation robots. The experimental results demonstrate that RED surpasses existing approaches (often by a significant margin) across critical metrics such as throughput, timing correctness, interference robustness, adaptability, and overhead.

智能机器人旨在有效地在充满移动机械元件和物体的动态且不可预测的环境中导航。这种由环境引起的动态情况，包括移动的障碍物，在运行期间很容易改变计算需求（例如，新任务的产生）和工作负载的结构（例如，任务之间的优先约束），从而对整体系统性能产生不利影响。当期望在资源和实时性有严格限制的机器人上进行多任务推理时，这一挑战会更加突出。为了应对这一挑战，我们引入了RED，这是一种系统性的实时调度方法，旨在支持资源有限的机器人系统中的多任务深度神经网络工作负载。它被设计为在遵循实时约束的同时自适应地管理机器人环境动态（RED）。RED的核心是一个基于截止时间的调度器，它采用一种中间截止时间分配策略，有效地管理复杂、不可预测环境所引发的工作负载变化和异步推理。这个调度框架还有助于灵活部署MIMONet（多输入多输出神经网络），这种网络在多任务机器人系统中通常用于规避内存瓶颈。基于这个调度框架，RED认识到并利用了MIMONet的一个独特特征：其权重共享架构。为了进一步适应和利用这一特性，RED设计了一种新颖且有效的工作负载细化和重构过程。这个过程确保了调度框架与MIMONet的兼容性并使效率最大化。我们已经在几个广泛使用的嵌入式和移动平台上实现了RED，包括NVIDIA Jetson Nano、TX2、Xavier和Orin平台。我们使用涵盖导航机器人中各种典型设置的工作负载评估了它的性能。实验结果表明，RED在吞吐量、定时正确性、抗干扰性、适应性和开销等关键指标上（往往大幅地）超越了现有方法。