Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Zhu, Xiaorui; Qiu, Chunxin; Deng, Fucheng; Pang, Su; Ou, Yongsheng

doi:10.1002/rob.21712

Cited by 8 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, cloud robotics, especially real-time cloud robotics, suffers from key limitations of network QoS (Wang et al, 2015) and privacy and security concerns (Kerns et al, 2014). Nonetheless, cloud potential has been expanding to push the boundaries of real-time robotics problems, such as localization (Zhu et al, 2017), collaborative 3D mapping (Mohanarajah et al, 2015b), and resource retrieval (Wang et al, 2014a). Our benefit of cloud robotics has been found in terms of utilizing cloud resources for offline multirobot simulations.…”

Section: Software Infrastructurementioning

confidence: 99%

Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots

Scherer,

Agrawal,

Best

et al. 2022

View full text Add to dashboard Cite

Subterranean robot exploration is difficult, with many mobility, communications, and navigation challenges that require an approach with a diverse set of systems, and reliable autonomy. While prior work has demonstrated partial successes in addressing the problem, here we convey a comprehensive approach to address the problem of subterranean exploration in a wide range of tunnel, urban, and cave environments. Our approach is driven by the themes of resiliency and modularity, and we show examples of how these themes influence the design of the different modules. In particular, we detail our approach to artifact detection, pose estimation, coordination, planning, control, and autonomy, and we discuss our performance in the tunnel, urban, and self-organized cave circuits of the DARPA Subterranean Challenge. Our approach led to a winning result in the tunnel circuit, and placing second in the urban circuit event. We convey lessons learned in designing and testing a resilient system for subterranean exploration that can generalize to a large range of operating conditions, and potential improvements for the future.

show abstract

Section: Software Infrastructurementioning

confidence: 99%

Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots

Scherer,

Agrawal,

Best

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Synthetic data simulating depth has been widely used in literature, being relatively simple to generate and reasonably similar to the data acquired with sensors; readers can refer to [21] for a recent survey. On the matter of cloud computing in AMR scenarios, [22] proposes the offloading of computationally demanding mapping and indoor localization tasks on local and remote cloud systems in order to ease tasks such as point cloud registration and filtering while [23] investigates the usage of cloud computing to track real-time an outdoor mobile robot by means of visual feature matching and a network-delay-compensation localization algorithm. Coupled with initial cloud-based training, our aim is to finally deploy the RNN network on a mobile robot endowed with an edge device capable of promptly fine-tuning the model to local real scenarios.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Edge Architecture for the Precise Positioning of Autonomous Mobile Robots through a Fine-Tuned RNN

Mutti,

Pedrocchi,

Dimauro

2024

Preprint

View full text Add to dashboard Cite

The precise localization of mobile robots in unstructured environments is of utmost importance for many industrial and field applications, especially when the mobile robot is part of a more complex kinematic chain, such as a mobile manipulator. Being able to precisely localize affects the outcome of tasks that rely on an open-loop kinematic computation, such as work-station docking procedures. To achieve a repeatable and precise localization and positioning, mobile robots generally rely on onboard sensors, most commonly 2D laser scanners, whose readings are subjected to noise and numerous disturbing factors (e.g., materials reluctance). Problems arise when precise localization is needed in dynamic and unstructured environments where generally applicable methods won’t perform adequately or might be time-consuming to set up. In this work, we propose a cloud-edge computing architecture to deploy a recurrent neural network (RNN) based registration system, which uses a pair of consecutive LiDAR readings to estimate a fixed transformation. The capability of RNNs to process contiguous inputs will help neglect errors embedded in punctual laser scanner readings and output a more precise registration estimation. In such a way, the RNN can estimate a displacement error based on multiple consecutive readings and act as a sensor to be employed in a closed-loop control scheme. To tackle the dynamic and unstructured environments, the model is firstly tuned on synthetic LiDAR data to embed rigid transformations into the deep learning model, for then rapidly fine-tuned on local scenarios. After model architecture and optimization of hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of the AMR(autonomous mobile robot) with that of a classical registration algorithm. The results suggest that an RNN model can greatly improve the registration precision of laser scanner signals and, consequently, the precise positioning efficiency of AMRs.

show abstract

“…Generally, the robots can access the developed cloud servers by wireless communication systems to obtain computing resources. [5][6][7] However, the developed cloud servers, for example, DAvinCi 8 and RoboEarth, 9 maybe unavailable in many scenes, such as disaster relief after an earthquake, in which the developed communication infrastructures, for example, 5G base station, are destroyed. Meanwhile, not all robots perform computing expensive algorithms all the time, which results in a waste of computing resources.…”

Section: Introductionmentioning

confidence: 99%

An intern-sufficient cloud for large-scale multi-robot systems and its application in multitarget navigation

Zhang

Zheng

Xiao-dong

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Due to generally limited computing capability of an individual robot, cloud-based robotic systems are increasingly used. However, applications in large-scale multi-robot systems will be hindered by communication congestion and consequent lack of computing resources. In this study, an intern-sufficient cloud is investigated to alleviate the burden of communication and thus support more robots. At the same time, it enables heterogeneously idle computing resources of robots inside the system to be shared on demand, instead of relying on cloud servers and communication infrastructures, to make the scope of application wider. To this end, a hierarchical communication mechanism and a resource schedule algorithm are proposed. In the mechanism, the transmission power, signal-to-noise ratio, available bandwidth, and other relevant features are taken into account to estimate link quality for data transmission. Then, the constrained communication conditions and heterogeneous computing resources are balanced by the resource scheduling algorithm, so that the most appropriate computing resources of the robots are contributed to the mobile cloud. Furthermore, a multitarget navigation task is applied on the cloud to validate the work. Thereby, simulations and experiments are performed. The results show that the proposed intern-sufficient cloud can provide stable resources of communication and computation for a multi-robot system with 20 physical robots while achieving more effective multitarget navigation.

show abstract

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cited by 8 publications

References 40 publications

Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots

Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots

Cloud-Edge Architecture for the Precise Positioning of Autonomous Mobile Robots through a Fine-Tuned RNN

An intern-sufficient cloud for large-scale multi-robot systems and its application in multitarget navigation

Contact Info

Product

Resources

About