Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform

Singhal, Aniruddha; Pallav, Prasun; Kejriwal, Nishant; Choudhury, Soumyadeep; Kumar, Swagat; Sinha, Rajesh Kumar

doi:10.1109/ecmr.2017.8098721

Cited by 28 publications

(12 citation statements)

References 26 publications

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“…In the Social Robotics domain, the outsourcing of services to third-party services in the cloud mostly concerns the aspects already mentioned in Section I: (i) dialogue [18,19], (ii) activity planning [20,21,22,23], (iii) collective learning [24,25,26,27], and (iv) perception [28,29,30,31].…”

Section: A Cloud-based Social Roboticsmentioning

confidence: 99%

Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud Services

Grassi¹,

Recchiuto²,

Sgorbissa³

2022

Preprint

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This article presents the design and the implementation of CAIR: a cloud system for knowledge-based autonomous interaction devised for Social Robots and other conversational agents. The system is particularly convenient for low-cost robots and devices. Developers are provided with a sustainable solution to manage verbal and non-verbal interaction through a network connection, with about 3,000 topics of conversation ready for "chit-chatting" and a library of pre-cooked plans that only needs to be grounded into the robot's physical capabilities. The system is structured as a set of REST API endpoints so that it can be easily expanded by adding new APIs to improve the capabilities of the clients connected to the cloud. Another key feature of the system is that it has been designed to make the development of its clients straightforward: in this way, multiple devices can be easily endowed with the capability of autonomously interacting with the user, understanding when to perform specific actions, and exploiting all the information provided by cloud services. The article outlines and discusses the results of the experiments performed to assess the system's performance in terms of response time, paving the way for its use both for research and market solutions. Links to repositories with clients for ROS and popular robots such as Pepper and NAO are given.

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Section: A Cloud-based Social Roboticsmentioning

confidence: 99%

Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud Services

Grassi¹,

Recchiuto²,

Sgorbissa³

2022

Preprint

View full text Add to dashboard Cite

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“…Figure 13. ROS general architecture of a multi-robot system, similar to the one presented in [34]. In this case, we show a fleet configuration with two UAVs; for larger fleets, the logic is the same.…”

Section: Realistic Simulationsmentioning

confidence: 99%

A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

et al. 2021

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This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.

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“…3D object models like Dex-Net 1.0 and Dex-Net 2.0. [47][48][49][50][51][52] (iv.) Automate industrial processes to Distributed robots' navigation increase the efficiency of manufacturing performance.…”

Section: Work Contribution Metrics and Evaluation Techniquesmentioning

confidence: 99%

“…The system was validated in a real industrial environment to safely perform obstacle avoidance with an autonomously driven forklift. Singhal et al [49] discuss a detailed implementation of a cloud-based system for managing a fleet of autonomous mobile robots operating in a factory or warehouse environment. The implemented system includes a global planner, which decides the target destination for each robot and along with the ground vehicles comprises a multi agent system (MAS), communicating with each other over a wireless network.…”

Section: Manufacture or Service Roboticsmentioning

confidence: 99%

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

2018

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Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.

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Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform

Cited by 28 publications

References 26 publications

Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud Services

Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud Services

A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

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