How Cloud Robotics Affects Complex And Tedious Tasks

DOI : 10.17577/NCRTCA-PID-081

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How Cloud Robotics Affects Complex And Tedious Tasks



PG Scholar Head of Department of MCA

Department of MCA Department of MCA

Dayananda Sagar College of Engineering Dayananda Sagar College of Engineering Bengaluru, Affiliated to VTU Bengaluru, Affiliated to VTU

Email ak0 6 14 10 @g m m Email –

  1. Cloud computing is a model that delivers computing resources and services over the internet. It provides on- demand access to storage, processing power, applications, and databases without the need for local infrastructure. Users can quickly scale resources, pay only for what they use, and access services from anywhere. Cloud computing offers scalability, cost savings, flexibility, reliability, and promotes innovation and faster time-to-market.

  2. Cloud robotics merges robotics and cloud computing to enhance robot capabilities. By leveraging cloud resources, robots can access immense computational power, data storage, and collaboration opportunities. Cloud robotics overcomes limitations of onboard computing, enabling robots to perform complex tasks, access vast information, and engage in collective learning. It finds applications in industrial automation, healthcare, transportation, and disaster response [1].

  3. Cloud robotics integrates robotics with cloud computing, allowing robots to access powerful cloud-based resources.

    By offloading computation, storage, and data processing to the cloud, robots can perform complex tasks, access vast amounts of information, and benefit from collective learning.Cloud robotics finds applications in industrial automation,

    healthcare, transportation, and disaster response. Challenges include latency, reliability, security, and privacy. Cloud robotics holds promise for advancing autonomous systems and revolutionizing various domains [2].

  4. Types of cloud robots so far:


    The architecture of cloud robotics consists of the following components:

    This architecture enables robots to leverage cloud resources for enhanced capabilities and collaboration while ensuring data security and efficient processing.

    1. Robots can earn from each other by sharing information in the cloud.

      1. Manufacturing

      2. Healthcare

      3. Agriculture

    • Improving healthcare accessibility and outcomes

    • Driving skills development and job opportunities


    Figure 4: Challenges faced by cloud robotics

    1. Network Dependence: Reliance on network connectivity for communication between robots and the cloud, making it susceptible to disruptions and latency issues.

    2. Data Security and Privacy: Ensuring the protection of sensitive data exchanged between robots and the cloud, preventing unauthorized access or misuse.

    3. Latency and Real-time Responsiveness: Delays in data transmission and processing, impacting real-time applications and time-critical tasks.

    4. Dependence on Cloud Infrastructure:

      Reliability and availability of cloud services can affect the operation of robots, requiring redundancy measures and backup solutions.

    5. Bandwidth Limitations: Limited network bandwidth may impede the streaming and processing of large amounts of data, affecting robot capabilities.

    6. Ethical and Legal Considerations: Addressing concerns regarding job displacement, ethical use, and regulatory compliance in cloud robotics deployments.

    7. Cost and Infrastructure Requirements: Investing in cloud resources, network infrastructure, and maintenance can pose financial challenges for implementing cloud robotics.


    Figure 5: Problems in cloud robotics

    1. Latency: When robots rely on cloud services for computation and decision-making, there can be a delay

      in transmitting data to and from the cloud. This latency can be problematic for tasks that require real-time responses, such as autonomous vehicles or industrial automation.

    2. Connectivity issues: Cloud robotics heavily relies on a stable and robust internet connection. If the connection is lost or disrupted, the robot's performance and functionality can be severely impacted.

    3. Security and privacy concerns: Storing and transmitting sensitive data to the cloud can raise security and privacy issues, especially in critical applications like medical or military robotics. Unauthorized access to cloud servers or data breaches could lead to serious consequences.

    4. Dependency on cloud infrastructure: Robots relying heavily on cloud services might become inoperable or severely limited if the cloud provider experiences downtime or other technical issues.

    5. Cost: Subscribing to cloud services and maintaining a reliable internet connection can be expensive, especially for small- scale robot deployments.

  8. SOLUTION: [3],[5]

    1. Edge computing: By performing critical computations closer to the robot instead of relying solely on the cloud, latency issues can be minimized. Edge computing allows robots to process data locally and make quicker decisions, reducing the reliance on constant internet connectivity.

    2. Hybrid approaches: Employ a combination of cloud- based processing and local processing to strike a balance between real-time responsiveness and utilizing cloud resources. Critical tasks can be handled locally, while non-time-sensitive tasks can be offloaded to the cloud.

    3. Robust connectivity: Invest in redundant and reliable internet connections to minimize the risk of connectivity issues. Additionally, consider offline capabilities for essential robot functions during temporary connectivity disruptions.

    4. Security measures: Implement robust encryption, authentication, and access control mechanisms to protect sensitive data and prevent unauthorized access. Regular security audits and updates to safeguard against emerging threats are crucial.

    5. Redundancy planning: Design robots with redundancy in mind so that they can continue functioning even if cloud services become temporarily unavailable. For example, essential features can be mirrored on the robot's onboard systems.

    6. Cost optimization: Consider the trade-offs between using cloud resources and on-device processing to minimize costs. Depending on the specific application, it might be more cost- effective to handle certain tasks locally rather than relying entirely on the cloud.

    7. Data optimization: Use data compression and data filtering techniques to reduce the amount of data transmitted between the robot and the cloud. This helps to lower latency and decrease bandwidth usage .


    improve perfrmance, identify anomalies, and optimize robotic operations.

Cloud robotics has various applications.

In conclusion, cloud robotics brings a host of benefits that significantly enhance the capabilities and efficiency of robotic systems. By tapping into the power of the cloud, robots can access vast computational resources, unlimited storage, and real- time collaboration. Continuous learning and improvement become possible, enabling robots to adapt and excel in various tasks. The ability to monitor and control robots remotely adds convenience and flexibility to their operation. Cloud robotics also offers scalability, costeffectiveness, and increased reliability, making it a promising and transformative technology for the future of robotics. As cloud computing continues to evolve, the potential for cloud robotics to revolutionize industries and daily life is truly exciting.

However, cloud robotics also poses challenges, including security and privacy concerns, latency issues in communication, and the need for robust and reliable network connectivity. Addressing these challenges and further advancing research in areas such as edge computing, human-robot interaction, ethics,

and standardization will be crucial for the widespread adoption and success of cloud robotics.

[1] Introduction to Cloud Robotics

Ricardo C. Mello, Moises R. N. Ribeiro,

Anselmo Frizera-Neto

[2] robotics-importance-and-challenges


[4] J. Wan, S. Tang, H. Yan, Di Li, S. Wang, A. V. Vasilakos, Cloud Robotics: Current Status and Open Issues

[5] Https:// o p ed m /d ef in ition /clo ud –

ro b o tics/

[6] of-cloud-robotics/

[7] Z. Vamossy and T. Haidegger, The rise of service robotics: Navigation in medical and mobile applications, in Proceedings of the 2014 IEEE 12th International Sym- posium on Applied Machine Intelligence and Informatics (SAMI), p. 11, Jan 2014

[8] In 2010, Willow Garage published a paper titled "Robot Operating System" that introduced the concept of the Robot Operating System (ROS).

[9] h ttps://go ld b erg .b er k /clo u d -ro bo tics/

[10] Chibani, A. et al.: Ubiquitous Robotics: Recent Challenges and future trends.

[11] h ttps://www.eesco rp o ratio n .com /clo u d


[12] Mohanarajah, G.; Hunziker, D.; DAndrea, R.; Waibel, M. Rapyuta: A cloud robotics platform. IEEE Trans. Autom. Sci. Eng. 2015, 12, 481493.

[13] Li, Ruijiao; Hu, Huosheng (16 October 2013). Towards ROS Based Multi-robot Architecture for Ambient Assisted Living. Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on. pp. 34583463.

[14] LaSelle, Rush. "Au to m atio n in th e Clo u d " . Robotic Industries Association. Retrieved 9 December 2014.