From Networked Robotics to Cloud and Big Data Supercharged Robotics: A Survey and Analysis

In recent years, with the prosperity of big data and cloud computing, robotics is evolving from conventional networked robotics to internet-scale connected, big data driven, and cloud resources supercharged multi-robot systems. In this survey paper, we present a survey of an advancing, pioneering, and multi-disciplinary field of research at the intersection of wireless sensor networks (WSN), robotics and big data. We discuss the concepts of networked robots. Networked robots refer to multiple robots working together in coordination with different types of embedded computers, sensors, and human users. Networked robotics allows multiple robots and supporting entities to execute tasks that are well beyond the capabilities of a single robot. The recent initiation of cloud technologies is opening new prospects for the provisioning of advanced robotic services based on the cooperation of some connected robots, smart environments and devices powered by the huge computational and storage capability of the cloud servers. We have recently witnessed the emergence of cloud computing on one hand and robotics platforms on the other hand. These two areas have been merging and resulting in the cloud robotics model to offer more distant services. Since networked robots require high computational and processing power, big data is becoming a dominant factor in networked and cloud robotics. This survey paper elaborates the primary concepts of networked and cloud robotics, their applications, challenges as well as the importance of big data in robotics, particularly, networked and cloud robotics and the noteworthy works in these areas.

An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks

Vernetzte Transportroboter in der Fabriklogistik*/Networked Robots for Factory Logistics – Factory Cloud and 4G/5G Campus Network for an Innovative Production Control and Robotic Logistics

Die Transformation zur digitalen und flexiblen Produktion ist für Unternehmen schwierig. Während die klassischen Wertschöpfungsprozesse ihrem Ende entgegensehen, müssen neue Produkte entwickelt und die Produktion geplant und vorbereitet werden. Dafür steht in Zukunft immer weniger Zeit zur Verfügung, sodass grundsätzlich völlig andere Fertigungs- und Logistikkonzepte in der Fabrik etabliert werden müssen. Eine besonders wandelbare und skalierbare Technologie bietet hier die mobile Robotik in Kombination mit einem privaten Mobilfunknetz und Edge-Cloud Rechenleistung.   The transformation from traditional to digital production is a significant effort for most production companies. As long-term value creation is depleting, new products and production processes have to be developed in a much shorter time. Completely new concepts for manufacturing and logistics are needed in the factories of the future to meet these requirements for flexible and scalable production. One possible strategy is the combination of mobile robots and mobile radio communication together with edge cloud computing resources.

A Hybrid Decentralized Coordinated Approach for Multi-Robot Exploration Task

Exploring the environment using multi-robot systems is a fundamental process that most automated applications depend on. This paper presents a hybrid decentralized task assignment approach based on Partially Observable Semi-Markov Decision Processes called HDec-POSMDPs, which are general models for multi-robot coordination and exploration problems in which robots can make their own decisions according to its local data with limited communication between the robot team. In this paper, a variety of multi-robot exploration algorithms and their comparison have been tackled. These algorithms, which have been taken into consideration, are dependent on different parameters. Collectively, there are five metrics maximize the total exploration percentage, minimize overall mission time, reduce the number of hops in the networked robots, reduce the energy consumed by each robot and minimize the number of turns in the path from the start pose cells to the target cells. Therefore, a team of identical mobile robots is used to perform coordination and exploration process in an unknown cell-based environment. The performance of the task depends on the strategy of coordination among the robots involved in the team. Therefore, the proposed approach is implemented, tested and evaluated in MRESim computer simulator, and its performance is compared with different coordinated exploration strategies for different environments and different team sizes. The experimental results demonstrate a good performance of the proposed approach compared to the four existing approaches.