Fog Robotics Algorithms for Distributed Motion Planning Using Lambda Serverless Computing

Ichnowski, Jeffrey; Lee, William; Murta, Victor; Paradis, Samuel; Alterovitz, Ron; Stoica, Ion; Goldberg, Ken

doi:10.1109/icra40945.2020.9196651

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…For instance, while the numerous functionalities of apps make it possible to implement fine-grained security policies, this also means that the number of entry points that might be targeted by attackers is increased. Implementing the best practices for serverless security is necessary in order to protect your application from being attacked [68,69].…”

Section: Serverless Best Practices For Any Cloudmentioning

confidence: 99%

The Rise of Serverless Architectures: Security Challenges and Best Practices

Cinar

2023

AJRCoS

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The field of serverless computing has had significant growth and recognition in the past decade. This emerging area has garnered attention because to its notable impact on cost reduction, latency reduction, scalability improvement, and elimination of server-side management, among other benefits. Nevertheless, there is still a dearth of comprehensive study that would facilitate developers and academics in gaining a more profound comprehension of the importance of serverless computing in many scenarios. Therefore, it is imperative to provide scholarly study data that has been published within this particular field. This study conducted a comprehensive analysis of 275 scholarly articles retrieved from reputable literature sources, with the aim of extracting valuable insights pertaining to serverless computing. Subsequently, the acquired data underwent analysis in order to address many study inquiries pertaining to the contemporary advancements in serverless computing, encompassing its fundamental principles, available platforms, and patterns of utilization, among other relevant aspects. In addition, we analyze the current obstacles confronting serverless computing and explore potential avenues for future research to facilitate its deployment and utilization.

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Section: Serverless Best Practices For Any Cloudmentioning

confidence: 99%

The Rise of Serverless Architectures: Security Challenges and Best Practices

Cinar

2023

AJRCoS

View full text Add to dashboard Cite

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“…Researchers have explored using the cloud for grasp planning (e.g., Kehoe et al [13], Tian et al [14], and Li et al [15]), parallelized Monte-Carlo grasp perturbation sampling [16], [17], [18], motion planning services (e.g., Lam et al [9]), and splitting motion plan computation between robot and cloud (e.g., Bekris et al [19] and Ichnowski et al [20]). Researchers also have explored using new cloud computing paradigms as they emerge, such as serverless computing [21], [22], in which algorithms run (and are charged) for short bursts of intensive computing; while others have explored using the cloud to gain access to hardware accelerators such as FPGAs [23]. Kehoe et al [24] survey the landscape of cloud robotics, including capabilities, potential applications, and challenges.…”

Section: A Design Principlesmentioning

confidence: 99%

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

Ichnowski¹,

Chen²,

Dharmarajan³

et al. 2022

Preprint

Self Cite

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Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run modern robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. In this paper, we present FogROS 2, an easy-to-use, open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS 2 provisions a cloud computer, deploys and launches ROS 2 nodes to the cloud computer, sets up secure networking between the robot and cloud, and starts the application running. FogROS 2 is completely redesigned and distinct from its predecessor to support ROS 2 applications, transparent video compression and communication, improved performance and security, support for multiple cloud-computing providers, and remote monitoring and visualization. We demonstrate in example applications that the performance gained by using cloud computers can overcome the network latency to significantly speed up robot performance. In examples, FogROS 2 reduces SLAM latency by 50%, reduces grasp planning time from 14s to 1.2s, and speeds up motion planning 28x. When compared to alternatives, FogROS 2 reduces network utilization by up to 3.8x. FogROS 2, source, examples, and documentation is available at https: //github.com/BerkeleyAutomation/FogROS2.

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“…Rather than forwarding the tasks of latency sensitive robotic applications to a centralized cloudlet or private cloud, they can be processed more efficiently at the edge network using fog nodes [148]. The idea of utilizing fog nodes in multi-agent cloud robotics is addressed in [149]- [153]. To summarize, although cloud resources offer higher computational power, they include additional delay for data transfer which is not suitable for latency sensitive tasks.…”

Section: A Resource Typementioning

confidence: 99%

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

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Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robotto-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

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Fog Robotics Algorithms for Distributed Motion Planning Using Lambda Serverless Computing

Cited by 17 publications

References 29 publications

The Rise of Serverless Architectures: Security Challenges and Best Practices

The Rise of Serverless Architectures: Security Challenges and Best Practices

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

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