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An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

Robotics engineering is gradually becoming an essential part of our daily life. However, it has been generating Big Data and seeking large computation cost because of the diversified sensors and processing requirements involved in heterogeneous robotics and their workflows. Therefore, cloud computing has become the incumbent platform for robotics. There are numerous works related to the architecture for cloud robotics. However, most cloud robotics architectures are ad hoc and are not based on a model. Inherent drawbacks of ad hoc approaches include being strictly domain specific and minimally customizable and adaptable. Moreover, heterogeneous cloud robotics platforms have been operating diverse requirements of industries and households. Nevertheless, there are certain benchmarks set to be achieved by Industry 4.0 and norms by Society 5.0. Those benchmarks and norms lead to new products and services in cross industries and alleviate the impending drawbacks. However, those should be achieved while retaining both the sovereignty and security of the respective systems and industries. This is equally applicable and an enormous challenge to the system-of-systems involving the cloud robotics domain. Therefore, we surveyed cloud robotics architectures. Then, we learned a top-down design approach involving a unified architectural framework as the cognitive approach for the highly variable and systematically complex challenges to be achieved in the next-generation cloud robotics domain. Reference architecture is a well-known approach for instantiating top-down unified architectural framework processes. Therefore, we proposed an architectural design process and modeling for the reference architecture for next-generation cloud robotics platforms.

Since the Internet of Robotic Things (IoRT) is composed of robots with actuators, interferences with the real-world activities are necessary, and safety is essential. In addition, some IoRT services may require bidirectional communication between multiple machines. One of the communication protocols that satisfy these requirements is AMQP, a broker architecture that emphasizes reliability and high functionality in communication. Therefore, using AMQP as the communication infrastructure between components in an IoRT system, it can contribute to improving the reliability of robot operations in IoRT and diversifying messaging between robots. To verify AMQP’s communication advantages, we have implemented the communication interface of AMQP in RT-Middleware, which is one of the robot middlewares, and have conducted performance and reliability tests on the effectiveness of RT-Middleware as a platform for constructing a reliable IoRT system. In the tests, we compared the communication performance of the platform with CORBA and MQTT communication interfaces implemented in RT-Middleware. The results show that although AMQP causes a small amount of latency compared to other communication interfaces, the distribution range of the latency is small, and relatively stable communication is performed. Furthermore, in the messaging quality test results, the data loss during communication by AMQP is almost a hundred times better than CORBA, and ten times better than MQTT, which mean that highly reliable messaging is possible. Based on this study’s findings, we conclude that AMQP should be fully used as a communication protocol for constructing IoRT systems.

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud–robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.

Networked robotics involves a collection of robots working together to perform complex tasks, such as search and rescue task in disaster management. Because such tasks are beyond the capacity of a single powerful robot, networked robotics has been widely researched. However, the modes of cooperation in traditional networked robotics have been restricted by the inherent physical constraint that all computations are performed in the robotic network, with knowledge sharing being limited to the collective storage in the network. Cloud robotics, which allows robots to benefit from the rich storage, computation, and communication resources of modern data centers, is widely accepted as a promising approach to efficient robot cooperation in applications, such as disaster management. In this paper, we study robotic cooperation in cloud robotics. We first give a conceptual view of the nature of this cooperation. We then propose three novel robotic cooperation frameworks for cloud robotics: robotic knowledge sharing cooperation, robotic physical-task cooperation, and robotic computation task cooperation. Finally, we identify several critical challenges, and illustrate the potential benefits of robotic cooperation in cloud robotics.

This study presents an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation using a JACO robotic arm with an Intel RealSense D435 camera. This system is designed to automate the manipulation of industrial valves by capturing point clouds (PCs) from multiple perspectives to improve the accuracy of pose estimation. The object recognition module includes scene segmentation, geometric primitives recognition, model recognition, and a color-based clustering and integration approach enhanced by a dynamic cluster merging algorithm. Pose estimation is achieved using the random sample consensus algorithm, which predicts position and orientation. The system was tested within a 60° field of view, which extended in all directions in front of the object. The experimental results show that the system performs reliably within acceptable error thresholds for both position and orientation when the objects are within a ±15° range of the camera's direct view. However, errors increased with more extreme object orientations and distances, particularly when estimating the orientation of ball valves. A zone-based dexterous manipulation strategy was developed to overcome these challenges, where the system adjusts the camera position for optimal conditions. This approach mitigates larger errors in difficult scenarios, enhancing overall system reliability. The key contributions of this research include a novel method for improving object recognition and pose estimation, a technique for increasing the accuracy of pose estimation, and the development of a robot motion model for dexterous manipulation in industrial settings. [ABSTRACT FROM AUTHOR]