Review of key technologies of climbing robots

Abstract

In recent years, the robot industry has developed rapidly, and researchers and enterprises have begun to pay more attention to this industry. People are barely familiar with climbing robots, a kind of special robot. However, from their practical value and scientific research value, climbing robots should studied further. This paper analyzes and summarizes the key technologies of climbing robots, introduces various kinds of climbing robots, and examines their advantages and disadvantages to provide a reference for future researchers. Many countries have studied climbing robots and made some achievements. However, due to the complexity of climbing robots, their climbing efficiency and accuracy need to be further improved. The new structure can improve the climbing efficiency. This paper analyzes climbing robots such as mechanical arms, magnetic attraction, and claws. Optimization methods and path planning can improve the accuracy of work. This paper involves some control methods, including complex intelligent control methods such as behavior-based robot control. This paper also investigates various kinematic planning methods and expounds and summarizes various path planning algorithms, including machine learning and reinforcement learning of artificial intelligence, ant colony algorithm, and other algorithms. Therefore, by analyzing the research status of climbing robots at home and abroad, this paper summarizes three important aspects of climbing robots, namely, structural design, control methods, and climbing strategies, elaborates on the achievements and existing problems of these key technologies, and looks forward to the future development trend and research direction of climbing robots.

Key words： climbing robot; mechanical structure design; control system; kinematics planning; path planning

Nomenclature

ACO	Ant colony
AMR	Autonomous mobile robot
APF	Artificial potential field
CNN	Convolution neural network
D‒H	Denavit–Hartenberg
MDP	Markov decision process
RL	Reinforcement learning

Acknowledgement

This research was funded by the National Natural Science Foundation of China (Grant No. 52075134).

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ye Dai, Shikun Li, Xukun Rui, Chaofang Xiang, and Xinlei Nie. The first draft of the manuscript was written by Ye Dai and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Conflict of Interest

The authors declare that they have no conflict of interest.

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