An official website of the United States government
Abstract The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must bescheduledandoptimizedto guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal ofcomputational awarenessin autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.
more »
« less
RTRBench: A Benchmark Suite for Real-Time Robotics
The emergence of “robotics in the wild” has triggered a wave of recent research in hardware and software to boost robots’ compute capabilities. Nevertheless, research in this area is hindered by the lack of a comprehensive benchmark suite. In this paper, we present RTRBench, a benchmark suite for robotic kernels. RTRBench includes 16 kernels, spanning the entire software pipeline of a wide swath of robots, all implemented in C++ for fast execution. Together with the suite, we conduct an evaluation of the workloads at the architecture level. We pinpoint the sources of inefficiencies in a modern robotic processor when executing the robotic kernels, along with the opportunities for improvements. The source code of the benchmark suite is available in https://cmu-roboarch.github.io/rtrbench/.
more »
« less
- Award ID(s):
- 2028949
- PAR ID:
- 10414987
- Date Published:
- Journal Name:
- International IEEE Symposium on Performance Analysis of Systems and Software
- Page Range / eLocation ID:
- 175 to 186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programmable Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained parallelism, low power consumption, reconfigurability, and concurrent execution. Even better, High-Level Synthesis (HLS) tools help bridge the gap between the non-trivial FPGA development efforts and rapid emergence of new GNN models. To enable investigation into how effectively modern HLS tools can accelerate GNN inference, we present GNNHLS, a benchmark suite containing a software stack for data generation and baseline deployment and FPGA implementations of 6 well-tuned GNN HLS kernels.more » « less
-
null (Ed.)Recent scientific computing increasingly relies on multi-scale multi-physics simulations to enhance predictive capabilities by replacing a suite of stand-alone simulation codes that independently simulate various physical phenomena. Inevitably, multi-physics simulation demands high performance computing (HPC) through advanced hardware and software accelerating due to its intensive computing workload and run-time communication needs. Thus, its research has become a hotspot across different disciplines. However, it is observed that most benchmarks used in the evaluation of corresponding work are through commercial or in-house codes. Then, the lack of accessible open-source multi-physics benchmark suites has presented a challenge in uniformly evaluating simulation performance across related disciplines. This work proposes the first open-source based benchmark suite with 12 selected benchmarks for research in multi-physics simulation, the Clarkson Open-Source Multi-physics Benchmark Suite (COMBS). Multiple metrics have been gathered for these benchmarks, such as instructions per second and memory usage. Also provided are build and benchmark scripts to improve usability. Additionally, their source codes and installation guides are available for downloading through a github repository built by the authors. The selected benchmarks are from key applications of multi-physics simulation and highly cited publications. It is believed that this benchmark suite will facilitate to harness the full potential of HPC research in the field of multi-physics simulation.more » « less
-
This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.more » « less
-
Quadrupeds are becoming increasingly popular in construction engineering research and practice for their affordability and accessibility. These robots navigate uneven terrain commonly found in construction sites, making them suitable vehicles for sensors and monitoring tasks. However, the lack of streamlined and fully developed client-side software packages inhibits rapid deployment of application-specific models to the field. Furthermore, substantial prerequisite knowledge of computer science and programming significantly impedes the ability of non-experts to adapt the robots to specific applications. In this work, we present a comprehensive framework to address these gaps in accessibility, enabling users to customize these robots to their needs. This framework provides a template that facilitates seamless communication between the robotic vehicle, edge devices, sensors, pathfinding algorithms, and a Unity simulation for mission planning and execution. As an example of this framework’s flexibility, we have conducted a case study using this template to demonstrate an application of the framework in the construction domain that performs worker activity recognition and features a novel self-labeling mechanism for construction activity video data. The findings highlight the potential of accessible software tools in expanding the utility of robotic platforms across various engineering domains.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISPASS55109.2022.00024
