Dear Colleagues,

The continuous development of robots offers a significant degree of convenience to our lives, but it also necessitates more stringent requirements for the technologies involved. This includes the continuous innovation and exploration of technological robots, in order to meet the specific needs of their rapid development.

In recent years, deep learning models represented by convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and graph neural networks (GNNs) have achieved remarkable progress in many fields. The neural network exhibits a strong data nonlinear fitting ability, feature extraction and representation ability; flexible structure design ability; and cross-scenario generalization capability. First, the deployment of an efficient sensor network is the cornerstone of intelligent robots, which helps to sense and supervise the entire task, and provide closed-loop feedback and improve the entire process. On the other hand, for various types of massive data generated in the sensing process, neural networks can cope well with and adapt to various tasks. However, the construction of the model structure, the selection of the objective function, the setting of hyperparameters, the establishment of the optimization algorithm, and the edge deployment of the trained deep learning model still hinder its practical application. In addition, the development of edge computing platforms, power consumption, and the storage limitations of mobile devices; fast data collection and processing methods; cloud–edge communication efficiency; and data privacy protection are all key factors restricting the development of this field. These important development directions involve many hot research fields, including wireless communication, signal processing, machine learning, automatic control, etc.

This Special Issue aims to provide a platform for researchers and application developers to discuss the opportunities, issues, challenges, and possible solutions for integrating deep learning and edge computing in intelligent robots. This Special Issue will provide an opportunity to present technical strategies and empirical evidence to propose new tools, methodologies, approaches, frameworks, and techniques to design solutions to deep learning-based intelligent computing challenges faced in the design and application of automatic robots. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Intelligent robot for underwater exploration;
• Visual robot for navigation;
• Deep learning-based intelligent control system;
• Reinforcement learning-based path planning;
• Development and application of inspection and maintenance robot;
• Image processing driven by confrontation generation network;
• Optimization and generalization in deep learning;
• Hyper-parameters setting in deep learning for robot control;
• Architecture design in deep learning for robot operation;
• Object detection;
• 3D reconstruction;
• Image classification.

Dr. Mingqiang Yang
Prof. Zhiguo Yu
Dr. Qinghe Zheng
Prof. Dr. Zhongjun Ding
Guest Editors

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• intelligent robot
• machine learning
• deep learning
• neural network
• automatic obstacle avoidance
• path planning
• 3D reconstruction
• object detection
• instance segmentation