Runtime Verification and Field-based Testing for ROS-based Robotic Systems
- URL: http://arxiv.org/abs/2404.11498v3
- Date: Wed, 21 Aug 2024 06:21:06 GMT
- Title: Runtime Verification and Field-based Testing for ROS-based Robotic Systems
- Authors: Ricardo Caldas, Juan Antonio Pinera Garcia, Matei Schiopu, Patrizio Pelliccione, Genaina Rodrigues, Thorsten Berger,
- Abstract summary: No clear guidance exists for architecting ROS-based systems to enable runtime verification and field-based testing.
This paper aims to fill this gap by providing guidelines to help developers and quality assurance (QA) teams develop, verify, or test their robots in the field.
- Score: 8.675312581079039
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robotic systems are becoming pervasive and adopted in increasingly many domains, such as manufacturing, healthcare, and space exploration. To this end, engineering software has emerged as a crucial discipline for building maintainable and reusable robotic systems. The robotics software engineering research field has received increasing attention, fostering autonomy as a fundamental goal. However, robotics developers are still challenged to achieve this goal because simulation cannot realistically deliver solutions to emulate real-world phenomena. Robots also need to operate in unpredictable and uncontrollable environments, which require safe and trustworthy self-adaptation capabilities implemented in software. Typical techniques to address the challenges are runtime verification, field-based testing, and mitigation techniques that enable fail-safe solutions. However, no clear guidance exists for architecting ROS-based systems to enable and facilitate runtime verification and field-based testing. This paper aims to fill this gap by providing guidelines to help developers and quality assurance (QA) teams develop, verify, or test their robots in the field. These guidelines are carefully tailored to address the challenges and requirements of testing robotics systems in real-world scenarios. We conducted (i) a literature review on studies addressing runtime verification and field-based testing for robotic systems, (ii) mined ROS-based applications repositories, and (iii) validated the applicability, clarity, and usefulness via two questionnaires with 55 answers overall. We contribute 20 guidelines: 8 for developers and 12 for QA teams formulated for researchers and practitioners in robotic software engineering. Finally, we map our guidelines to open challenges in runtime verification and field-based testing for ROS-based systems, and we outline promising research directions in the field.
Related papers
- RAMPA: Robotic Augmented Reality for Machine Programming and Automation [4.963604518596734]
This paper introduces Robotic Augmented Reality for Machine Programming (RAMPA)
RAMPA is a system that utilizes the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3.
Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment.
arXiv Detail & Related papers (2024-10-17T10:21:28Z) - Tiny Robotics Dataset and Benchmark for Continual Object Detection [6.4036245876073234]
This work introduces a novel benchmark to evaluate the continual learning capabilities of object detection systems in tiny robotic platforms.
Our contributions include: (i) Tiny Robotics Object Detection (TiROD), a comprehensive dataset collected using a small mobile robot, designed to test the adaptability of object detectors across various domains and classes; (ii) an evaluation of state-of-the-art real-time object detectors combined with different continual learning strategies on this dataset; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field.
arXiv Detail & Related papers (2024-09-24T16:21:27Z) - A Roadmap for Simulation-Based Testing of Autonomous Cyber-Physical Systems: Challenges and Future Direction [5.742965094549775]
This paper pioneers a strategic roadmap for simulation-based testing of autonomous systems.
Our paper discusses the relevant challenges and obstacles of ACPSs, focusing on test automation and quality assurance.
arXiv Detail & Related papers (2024-05-02T07:42:33Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks.
As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments.
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
arXiv Detail & Related papers (2023-12-14T10:02:55Z) - Security Challenges in Autonomous Systems Design [1.864621482724548]
With the independence from human control, cybersecurity of such systems becomes even more critical.
With the independence from human control, cybersecurity of such systems becomes even more critical.
This paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions.
arXiv Detail & Related papers (2023-11-05T09:17:39Z) - Tiny Robot Learning: Challenges and Directions for Machine Learning in
Resource-Constrained Robots [57.27442333662654]
Machine learning (ML) has become a pervasive tool across computing systems.
Tiny robot learning is the deployment of ML on resource-constrained low-cost autonomous robots.
Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints.
This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
arXiv Detail & Related papers (2022-05-11T19:36:15Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Dual-Arm Adversarial Robot Learning [0.6091702876917281]
We propose dual-arm settings as platforms for robot learning.
We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
arXiv Detail & Related papers (2021-10-15T12:51:57Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Integrated Benchmarking and Design for Reproducible and Accessible
Evaluation of Robotic Agents [61.36681529571202]
We describe a new concept for reproducible robotics research that integrates development and benchmarking.
One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible.
We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
arXiv Detail & Related papers (2020-09-09T15:31:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.