Convergence of Machine Learning and Robotics Communication in Collaborative Assembly: Mobility, Connectivity and Future Perspectives

Alsamhi, Saeed Hamood; Ma, Ou; Ansari, Mohd. Samar

doi:10.1007/s10846-019-01079-x

Cited by 59 publications

(41 citation statements)

References 113 publications

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“…3. Furthermore, the authors [42] discussed the convergence of several machine learning techniques for path planning effectively and efficiently to enhance the connectivity, mobility, and QoS. A group of drones represents a cluster which uses the Flying Ad-hoc NETwork (FANET) technology for communication among its members, and also for sharing data gathered (by the onboard IoT devices) with other IoT devices on the ground.…”

Section: A Smart Dronesmentioning

confidence: 99%

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

et al. 2019

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Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smartcity applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

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Section: A Smart Dronesmentioning

confidence: 99%

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

et al. 2019

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“…Drones are consider as space robots 42 and categorize under the low altitude platform (LAP) 43,44 . Drones have some significant benefits for a communication network such as mobility management, 45,46 small propagation delay, low cost, rapid deployment, line of sight (LoS), static station, and so on, which are discussed in Reference 47. drones are emerged as an attractive service provider in disaster regions for emergency communication due to their fast deployment and easy to operate them from their prevailing portable handsets 6,48 .…”

Section: Related Workmentioning

confidence: 99%

Blockchain mechanism with Byzantine fault tolerance consensus for Internet of Drones services

Singh

Venkatesan

2021

Trans Emerging Tel Tech

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The increasing number of drones brings various issues like security risks, increasing air traffic, misuse or crash of drones, and so on. Blockchain can provide the solution to mitigate these problems of drones. Hence, in this article, we have presented the Blockchain of Drones (BCoD), a conceptual approach of collaboration between blockchain and IoD to make drones advantageous through blockchain properties. But the main challenge for adopting blockchain is scalability as consensus in blockchain relies on the message communications by replicated nodes to attain flexibility against faulty nodes. Considering this, we present an advanced byzantine fault tolerance (ABFT) consensus for drone‐based applications. This protocol provides scalability with minimum cost and resources for achieving general agreement. It uses only f + 1 resources in normal case and 2f + 1 resources in faulty case with O(n) communication complexity. The systematic analysis and evaluation show that the ABFT improves the performance of the system for a wide range of networks compared with existing BFT systems.

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“…The field of robotics is growing at a great pace [1], [2], and one factor driving its growth is the widening range of robotic applications [3]. Recent advancements in machine learning [4] are being used to develop smarter robots, and so most of these robotic applications require high-performing computational capabilities to attain a satisfactory level of performance [5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Dynamic Computational Offloading Method for Cloud Robotics

Penmetcha

Min

2021

IEEE Access

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Robots come with a variety of computing capabilities, and running computationallyintense applications on robots is sometimes challenging on account of limited onboard computing, storage, and power capabilities. Meanwhile, cloud computing provides on-demand computing capabilities, and thus combining robots with cloud computing can overcome the resource constraints robots face. The key to effectively offloading tasks is an application solution that does not underutilize the robot's own computational capabilities and makes decisions based on crucial cost parameters such as latency and CPU availability. In this paper, we formulate the application offloading problem as a Markovian decision process and propose a deep reinforcement learning-based deep Q-network (DQN) approach. The statespace is formulated with the assumption that input data size directly impacts application execution time. The proposed algorithm is designed as a continuous task problem with discrete action space; i.e., we apply a choice of action at each time step and use the corresponding outcome to train the DQN to acquire the maximum rewards possible. To validate the proposed algorithm, we designed and implemented a robot navigation testbed. The results demonstrated that for the given state-space values, the proposed algorithm learned to take appropriate actions to reduce application latency and also learned a policy that takes actions based on input data size. Finally, we compared the proposed DQN algorithm with a long short-term memory (LSTM) algorithm in terms of accuracy. When trained and validated on the same dataset, the proposed DQN algorithm obtained at least 9 percentage points greater accuracy than the LSTM algorithm.INDEX TERMS Cloud robotics, deep reinforcement learning, deep Q-networks (DQN), AWS, neural networks, application offloading, robot navigation.

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Convergence of Machine Learning and Robotics Communication in Collaborative Assembly: Mobility, Connectivity and Future Perspectives

Cited by 59 publications

References 113 publications

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

Blockchain mechanism with Byzantine fault tolerance consensus for Internet of Drones services

A Deep Reinforcement Learning-Based Dynamic Computational Offloading Method for Cloud Robotics

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