Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition

Dear Colleagues,

This publication is a continuation of our previous Special Issue on the same topic, entitled “Advances in Mobile Robot Perceptions, Planning, Control and Learning”.

With the increasing demand for mobile robots, such as lunar rovers, unmanned driving vehicles, rescue robots, and delivery robots, in the fields of aerospace, terrain, surface, and underwater, there is a growing interest in the development of new technologies that can be used to advance state-of-the-art mobile robots. A reliable mobile manipulation consists of at least three core parts: the perception of the environment, motion planning, and control. When detecting the environment through sensing (LiDAR, radar, camera, GPS, IMU, etc.), it is important to design a planner through various theoretical approaches (machine learning, reinforcement learning, convex/nonconvex optimization, evolutionary computation, potential field methods, etc.) to find the optimal trajectory of a mobile robot while avoiding static and/or dynamic obstacles. Additionally, it is necessary to design a robust controller (sliding mode control, model predictive control, adaptive neural network control, etc.) for the robot in perturbed environments, such as complex terrains and external contact forces.

It is expected that mobile robots can tackle the designed tasks (grasping, autonomous driving, etc.) under diverse and unstructured environmental conditions, but this brings challenges for sensing, planning, and control. For this reason, the perception, implementation, modeling, control, and learning of mobile robots have become urgent issues. We welcome original research contributions and state-of-the-art reviews of both theoretical and experimental studies which promote further research activities in this area.

The main topics of this Special Issue include, but are not limited to, the following:

• Mobile robot intelligent perception and control;
• Visual or haptic control with sensor feedback;
• Human–robot interaction or teleoperation control;
• Motion planning and navigation indoors or outdoors;
• Machine learning for object detection, recognition, and tracking;
• Reinforcement/imitation/transfer learning for mobile robots;
• Multimodal learning for mobile robots;
• Advanced modeling and sensors for mobile manipulation;
• Applications of mobile manipulation.

Dr. Yingbai Hu
Dr. Chao Zeng
Dr. Shu Li
Prof. Dr. Alois Christian Knoll
Guest Editors

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• mobile robot
• human-robot interaction
• object detection, recognition, and tracking
• robot control
• motion planning and navigation

• Advances in Mobile Robot Perceptions, Planning, Control and Learning in Sensors (15 articles)