Collaborative Optimization Control of Gravity Center and Pose of Hexapod Robot in Complex Terrains

The adaptability of a hexapod robot to complex terrain is highly dependent on its own posture, which directly affects its stability and flexibility. In order to adapt to a change in terrain, it is necessary to adjust posture in real time when walking. At the same time, external factors such as ground state and landing impact will also interfere with posture. Therefore, it is necessary to maintain balance after adjustment. This paper proposes a pose adjustment method utilizing joint angle control. It enhances robot stability, flexibility, and terrain adaptability through torso posture and center of gravity optimization, aiming to maintain balance. The strategy’s effectiveness was validated via Adams–Simulink co-simulation. Optimal position and posture adjustment for the torso was then implemented at the six-legged support stage after each step, employing inverse kinematics and a triangular gait. It is found that without pose adjustment, the direction deviation will accumulate and significantly deviate from the trajectory. The introduction of this adjustment can effectively correct the direction deviation and torso posture angle, increase the stability margin, ensure stable straight-line walking, and significantly reduce joint energy consumption. Crawling experiments with the physical prototype further validate the strategy. It rapidly counters instantaneous attitude fluctuations during leg alternation, maintaining a high stability margin and improving locomotion efficiency. Consequently, the robot achieves enhanced directional stability, overall stability, and energy efficiency when traversing terrain.

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Publication Details

Journal
Machines
Published
2025-09-18
DOI
https://doi.org/10.3390/machines13090871
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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article

Collaborative Optimization Control of Gravity Center and Pose of Hexapod Robot in Complex Terrains

Xintian Liu, Diqing Fan, Chenjiang Yu
1 citations
Machines
Robotic Path Planning Algorithms
1.28
article

Collaborative Optimization Control of Gravity Center and Pose of Hexapod Robot in Complex Terrains

Xintian Liu, Diqing Fan, Chenjiang Yu
article en
1 citations

Abstract

The adaptability of a hexapod robot to complex terrain is highly dependent on its own posture, which directly affects its stability and flexibility. In order to adapt to a change in terrain, it is necessary to adjust posture in real time when walking. At the same time, external factors such as ground state and landing impact will also interfere with posture. Therefore, it is necessary to maintain balance after adjustment. This paper proposes a pose adjustment method utilizing joint angle control. It enhances robot stability, flexibility, and terrain adaptability through torso posture and center of gravity optimization, aiming to maintain balance. The strategy’s effectiveness was validated via Adams–Simulink co-simulation. Optimal position and posture adjustment for the torso was then implemented at the six-legged support stage after each step, employing inverse kinematics and a triangular gait. It is found that without pose adjustment, the direction deviation will accumulate and significantly deviate from the trajectory. The introduction of this adjustment can effectively correct the direction deviation and torso posture angle, increase the stability margin, ensure stable straight-line walking, and significantly reduce joint energy consumption. Crawling experiments with the physical prototype further validate the strategy. It rapidly counters instantaneous attitude fluctuations during leg alternation, maintaining a high stability margin and improving locomotion efficiency. Consequently, the robot achieves enhanced directional stability, overall stability, and energy efficiency when traversing terrain.

MachinesVol. 13(9)
Shanghai University of Engineering Science (CN)
Openalex Percentile: Top 16%
Robotic Path Planning Algorithms
1.28
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