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. 2025 Jul 11;25(14):4331.
doi: 10.3390/s25144331.

CPG-Based Control of an Octopod Biomimetic Machine Lobster for Mining Applications: Design and Implementation in Challenging Underground Environments

Jianwei Zhao  1 , Haokun Zhang  1 , Mingsong Bao  2 , Boxiang Yin  3 , Yiteng Zhang  4 , Zhen Jiang  1
Affiliations

CPG-Based Control of an Octopod Biomimetic Machine Lobster for Mining Applications: Design and Implementation in Challenging Underground Environments

Jianwei Zhao et al. Sensors (Basel). .

Abstract

Central pattern generators (CPGs) have been extensively researched and validated as a well-established methodology for bionic control, particularly within the field of legged robotics. However, investigations concerning octopod robots remain relatively sparse. This study presents the design of an octopod robotic system inspired by the biological characteristics of lobsters. The machine lobster utilizes remote sensing technology to execute designated tasks in subterranean and mining environments, with its motion regulated by CPGs, accompanied by a comprehensive simulation analysis. The research commenced with the modeling of a biomimetic lobster robot, which features a three-degree-of-freedom leg structure and torso, interconnected by shape memory alloys (SMAs) that serve as muscle actuators. Mathematically, both forward and inverse kinematics were formulated for the robot's legs, and a 24-degree-of-freedom (DOF) gait pattern was designed and validated through MATLAB 2020a simulations. Subsequently, a multi-layer mesh CPG neural network model was developed utilizing the Kuramoto model, which incorporated frustration effects as the rhythm generator. The control model was constructed and evaluated in Simulink, while dynamic simulations were conducted using Adams 2022 software. The findings demonstrate the feasibility, robustness, and efficiency of the proposed CPG network in facilitating the forward locomotion of the lobster robot, thereby broadening the range of control methodologies applicable to octopod biomimetic robots.

Keywords: CPG control; Kuramoto model; gait design; octopod bionic robot; shape memory alloys.

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Conflict of interest statement

Mingsong Bao was employed by the Shandong Guoxing Intelligent Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(a) Overall view of the machine lobster. (b) Structural view of the legs of the machine lobster (the dashed line in the figure represents SMA).
Figure 2
Figure 2
Schematic of joint control of SMAs.
Figure 3
Figure 3
DH coordinate system diagram of the leg structure.
Figure 4
Figure 4
Schematic diagram of monopodial gait of machine lobster.
Figure 5
Figure 5
(a) Octopod gait diagram. (b) Octopod gait timing diagram of the machine lobster.
Figure 6
Figure 6
(a) Diagram of the corner versus end-of-foot position. (b) Diagram of the corner versus end-of-foot position after entering monopodial gait. (c) Relationship of the corner versus end-of-foot position after entering monopodial gait. (d) Relationship of the corner versus end-of-foot position after entering monopodial gait. (e) Relationship between the corner and end-of-foot position after entering monopodial gait. (f) Relationship between the corner and foot-end position after entering monopodial gait.
Figure 7
Figure 7
Multi-layer CPG network proposed by McCrea, D. et al. [30].
Figure 8
Figure 8
(a) Chain structure of interneurons. (b) Mesh structure of interneurons.
Figure 9
Figure 9
Difference between (a,b) chain structure of interneurons and (c,d) mesh structure of interneurons.
Figure 10
Figure 10
Schematic diagram of CPG control structure for machine lobsters.
Figure 11
Figure 11
(a) Output of four rhythms. (b) Output of thoracic axis joint angles. (c–f) Variation of each joint angle under the control of the four rhythms according to the octopod gait.
Figure 12
Figure 12
Adams simulation of machine lobster walking.
Figure 13
Figure 13
Adams simulation of robotic lobster obstacle crossing walking.
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