Abstract
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.
Highlights
Scientific research on mobile robots is expanding rapidly
Incremental learning can incrementally add the unknown objects in the known list by training the convolutional neural network (CNN) model repeatedly. From this point of view, our goal is to develop an incremental learning method using a one-class support vector machine (SVM)based outlier detection model by the support vector data description (SVDD) method [10]
This paper proposed an efficient object segmentation model for separating multiple objects of robotic view-frame and SVDD-based unknown object detection for avoiding collisions between objects and obstacles in an environment for a mobile cloud–robot application
Summary
Scientific research on mobile robots is expanding rapidly. These robots help humans in many application areas and make our daily work easier. Due to their flexibility, such as moving in an indoor or outdoor place autonomously and performing their target tasks, these robots can perform many tasks in the areas of transportation, personal services, construction, medical care, patrolling, museum guides, emergency rescue industrial automation, petrochemical applications, intervention in extreme environments, reconnaissance, operations, planetary exploration, entertainment, and surveillance. Most of this work is done autonomously without any human assistance, which is the biggest advantage of these mobile robots [1]. The assigned tasks are properly done with the help of the assistant software and control unit that are part of the mobile robot.
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