Safety and resiliency are essential components of autonomous vehicles. In this research, we introduce ROSFI, the first robot operating system (ROS) resilience analysis methodology, to assess the effect of silent data corruption (SDC) on mission metrics. We use unmanned aerial vehicles (UAVs) as a case study to demonstrate that system-level parameters, such as flight time and success rate, are necessary for accurately measuring system resilience. We demonstrate that downstream ROS tasks such as planning and control are more susceptible to SDCs than the visual perception stage in the perception–planning–control (PPC) compute pipeline. This observation only becomes apparent when we consider the complete end-to-end system-level pipeline, as opposed to isolated compute kernels, as previous work does. To enhance the safety and robustness of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a computational overhead of no more than 0.0062%. Finally, our open-source methodology can be utilized to comprehensively test the robustness of other ROS-based applications. It is available for public download athttps://github.com/harvard-edge/MAVBench/tree/mavfi.

Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess the effect of silent data corruption (SDC) on UAVs' mission metrics, such as flight time and success rate, for accurately measuring system resilience. To enhance the safety and resilience of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a compu-tational overhead of no more than 0.0062%. Our application-aware resilience analysis framework, MAVFI, can be utilized to comprehensively test the resilience of other Robot Operating System (ROS)-based applications and is publicly available at https://github.com/harvard-edge/MAVBench/tree/mavfi.

A key challenge for hardware acceleration of robotics applications is the enormous diversity of possible deployment scenarios. To create efficient accelerators while minimizing non-recurring engineering costs, it is essential to identify high-level computational patterns that are prescribed by the physical characteristics of the deployed robot system and directly embed these domain-specific insights into the accelerator design process. To address this challenge, we present RoboShape, an accelerator framework that leverages two topology-based computational patterns that scale with robot size: (1) topology traversals, and (2) large topology-based matrices. Using these patterns and building on prior work, we expose opportunities to directly use robot topology to inform architectural mechanisms including task scheduling and allocation, data placement, block matrix operations, and sparse I/O data. Designing architectures according to topology-based patterns enables flexible, scalable, optimized accelerator deployment across the nonlinear design space of robot shape and computing resources. With this insight, we establish a systematic framework to generate accelerators, and use it to implement three accelerators for three different robots, achieving speedups over state-of-the-art CPU and GPU solutions. For the topologically-diverse iiwa manipulator, HyQ quadruped, and Baxter torso robots, RoboShape accelerators on an FPGA provide a 4.0× to 4.4× speedup in compute latency over CPU and a 8.0× to 15.1× speedup over GPU for the dynamics gradients, a key bottleneck preventing online execution of nonlinear optimal motion control for legged robots. Taking a broader view, for topology-based applications, RoboShape enables analysis of performance and resource utilization tradeoffs that will be critical to managing resources across accelerators in future full robotics domain-specific SoCs.

We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory opti-mization subproblem used in state-of-the-art robotic planning, control, and machine learning, which requires tens to hundreds of naturally parallel computations of rigid body dynamics and their gradients at each iteration. GRiD leverages URDF parsing and code generation to deliver optimized dynamics kernels that not only expose GPU-friendly computational patterns, but also take advantage of both fine-grained parallelism within each computation and coarse-grained parallelism between computations. Through this approach, when performing multiple computations of rigid body dynamics algorithms, GRiD provides as much as a 7.2x speedup over a state-of-the-art, multi-threaded CPU implementation, and maintains as much as a 2.5x speedup when accounting for I/O overhead. We release GRiD as an open-source library for use by the wider robotics community.

The limited onboard energy of autonomous mobile robots poses a tremendous challenge for practical deployment. Hence, efficient computing solutions are imperative. A crucial shortcoming of state-of-the-art computing solutions is that they ignore the robot’s operating environment heterogeneity and make static, worst-case assumptions. As this heterogeneity impacts the system’s computing payload, an optimal system must dynamically capture these changes in the environment and adjust its computational resources accordingly. This paper introduces RoboRun, a mobile-robot runtime that dynamically exploits the compute-environment synergy to improve performance and energy. We implement RoboRun in the Robot Operating System (ROS) and evaluate it on autonomous drones. We compare RoboRun against a state-of-the-art static design and show 4.5X and 4X improvements in mission time and energy, respectively, as well as a 36% reduction in CPU utilization.

High-level synthesis (HLS) tools almost universally generate statically scheduled datapaths. Static scheduling implies that circuits out of HLS tools have a hard time exploiting parallelism in code with potential memory dependencies, with control-dependent dependencies in inner loops, or where performance is limited by long latency control decisions. The situation is essentially the same as in computer architecture between Very-Long Instruction Word (VLIW) processors and dynamically scheduled superscalar processors; the former display the best performance per cost in highly regular embedded applications, but general purpose, irregular, and control-dominated computing tasks require the runtime flexibility of dynamic scheduling. In this work, we show that high-level synthesis of dynamically scheduled circuits is perfectly feasible by describing the implementation of a prototype synthesizer which generates a particular form of latency-insensitive synchronous circuits. Compared to a commercial HLS tool, the result is a different trade-off between performance and circuit complexity, much as superscalar processors represent a different trade-off compared to VLIW processors: in demanding applications, the performance is very significantly improved at an affordable cost. We here demonstrate only the first steps towards more performant high-level synthesis tools adapted to emerging FPGA applications and the demands of computing in broader application domains.