Development and Implementation of the Underwater Robot Enhanced by AI Methods

Artificial Intelligence Robotics Signal Processing

Dear Colleagues,

With the rapid development of the global economy and technology, there is an increasing demand for tasks such as underwater operations and exploration. Underwater robots, as an advanced underwater work tool, are undertaking increasingly underwater work tasks and have received rapid development and high attention from countries around the world.

To better complete tasks, underwater robots must consider different types of uncertainties and achieve efficient and robust interaction between the environment and the robot. Underwater robots should be able to make autonomous decisions to reduce the burden on operators. For example, robots can automatically adjust their motion control mode, select suitable sensors for data collection, or perform basic task planning based on environmental conditions and task requirements. In addition, virtual reality technology, posture recognition and other technologies can provide operators with more intuitive and immersive underwater robot operating experience. With the development of artificial intelligence technology and machine learning technology, underwater robots will continue to become intelligent, possessing higher levels of autonomous decision-making, autonomous control, and task planning capabilities. At the same time, they will also have multi-agent collaboration functions, which can achieve cooperation, joint cruising, and task execution among multiple robots.

This research topic is dedicated to collecting the latest development and research findings, addressing theoretical and practical issues related to advanced methods, path planning, adaptive control, and signal processing in underwater robot systems. The aim is to provide a platform for researchers and practitioners to showcase and discuss innovative solutions.

Topics of interest include, but are not limited to:

  • Underwater robot control system based on deep learning or reinforcement learning.

  • Teleoperation control of ROV/AUV/AGV

  • Multimodal data fusion for underwater robot control

  • Path planning for ROV/AUV/AGV

  • Collaborative or semi-autonomous control of multiple robots

  • Posture recognition and localization

  • Intelligent wearable and assistive robot devices

Dr. Zhan Li

Dr. Hongliang Guo

Dr. Weibing Li

Dr. Chunxu Li