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.

翻译：智能机器人被设计用于有效导航充满移动机械元素和物体的动态且不可预测的环境。此类由环境引发的动力学（包括移动障碍物）可能在运行时轻易改变计算需求（例如新任务的创建）和工作负载结构（例如任务间的优先级约束），从而对整体系统性能产生不利影响。当预期在资源受限且实时约束严苛的机器人上执行多任务推理时，这一挑战更加凸显。为应对此挑战，我们提出RED——一种系统化实时调度方法，专用于在资源受限的机器人系统中支持多任务深度神经网络工作负载。该方法旨在自适应管理机器人环境动力学（RED），同时满足实时约束。RED的核心是一种基于截止时间的调度器，采用中间截止时间分配策略，有效管理由复杂、不可预测环境引发的工作负载变化和异步推理。此调度框架还支持多输入多输出神经网络（MIMONet）的灵活部署，而这类网络常用于多任务机器人系统以避免内存瓶颈。基于该调度框架，RED识别并利用MIMONet的一个独特特性：其权重共享架构。为进一步适应并发挥这一特性，RED设计了一种新颖且高效的工作负载细化与重构过程，确保调度框架与MIMONet的兼容性并最大化效率。