Robot autonomy is driving an ever-increasing demand for computational power, including on-board multi-core CPUs and accelerators such as GPUs, to enable fast perception, planning, control, and more. Careful scheduling of these computational tasks on the CPU cores and GPUs is important to prevent locking up the finite computational capacity in ways that hinder other critical workloads; delays in computing time-critical tasks like obstacle detection and control can have huge negative consequences for autonomous robots, potentially resulting in damage, substantial financial loss, or even loss of life. In this paper, we leverage recent advances from real-time systems research. We apply TimeWall, a component-based real-time framework, to the computational components of an autonomous drone and experimentally show that the timeliness and safe operation properties of a drone are preserved even in the presence of increasing interfering computational processes.

The Robot Operating System 2 (ROS) is heavily used in autonomous systems due to its large ecosystem and modular design. However, ROS remains problematic for real-time applications despite prior efforts to improve its real-time capabilities. Many of these problems are rooted in the implementation of the ROS executor, which does not support preemption or userspecified priorities. These properties are fundamental to real-time scheduling and desirable for general systems to reduce latency. ROS variants in prior work have separately supported the preemption and prioritization of callbacks but place restrictions on the application, preventing their adoption in a real-world workload. This paper addresses these deficiencies with a novel executor framework,$\operatorname{ROS}^{\text{RT}}$, which is compatible with any type of ROS application while supporting preemptive, priority-driven scheduling. Additionally, to support flexible EDF scheduling in ROS${ }^{\text {RT }}$, a custom EDF scheduler implementation is proposed using the new Linux scheduling class SCHED_EXT. ROS${ }^{\text {RT }}$s flexibility and real-time compatibility do not come at the cost of overheads: it achieves a significant decrease in publisher-tosubscriber overhead from the native ROS executor. Finally, this paper concludes with a case study performed on the Autoware Reference System, which simulates the execution of the LiDAR module in an autonomous driving application, demonstrating the ability of$\operatorname{ROS}^{\text{RT}}$at real-time scheduling in real-world scenarios.

It is common practice today to design complex safety-critical systems by repurposing hardware and software components originally designed for other contexts and using such components in a "black-box" fashion. However, if a black box’s inner workings are not fully understood, then this can be unsafe. This paper reports on an investigation pertaining to a black box that is important for autonomous systems, namely NVIDIA’s CUDA GPU framework. This investigation was motivated by certain timing glitches in CUDA kernels reported in the literature. After extensive tracing and testing efforts, the culprit causing these glitches was surprisingly found to be not CUDA-related at all, but rather delays due to system management interrupts (SMIs), a known source of timing unpredictability on x86 machines that is rarely if ever mentioned in work on real-time GPU usage. The effects of these SMIs are invisible to the operating system and can cause all cores on an x86 machine to become unavailable for over 20ms! This paper describes the methods used to uncover this timing-glitch source. It also discusses some lessons learned when trying to validate the timing behavior of black-box components.