Autonomous Excavation Trajectory Generation for Trenching Tasks Based on Skills of Skillful Operator

Zhao, Jiangying; Tan, P.; Hu, Yongbiao; Tian, Mingrui; Xia, Xiaohua

doi:10.1109/icmee56406.2022.10093336

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trajectory of the autonomous excavator is planned based on the needs of the work. 28,29 This section defines the point-to-point trajectory path of the manipulator bucket's tooth. In this section, the interpolation method is selected to calculate the trajectory planning.…”

Section: Control Objectivementioning

confidence: 99%

See 1 more Smart Citation

Research on the motion control strategy of autonomous excavator based on online compensation

Gong,

Zhang,

Jin

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The motion control accuracy of the excavator manipulator is the primary guarantee for autonomous excavators to complete work tasks. This paper studies the motion control strategy of the manipulator of a certain autonomous excavator. By establishing a dynamic model of a 3-degree of freedom of excavator manipulator, the gravity term, inertia term, and centripetal force term in the model are equivalent to external disturbances for online compensation, which improves the motion control accuracy of the excavator manipulator. Based on the dynamic model, a control method of the bucket tooth tip trajectory of an autonomous excavator is proposed. The simulation and test results show that the maximum tracking errors of the joint angles of the boom, the bucket, and the bucket are reduced by 43.14, 26.56, and 51.05% respectively, and the average errors of the whole trajectory are reduced by 56.41, 61.33, and 64.26% respectively. The simulation and test results show that the proposed motion control strategy improves the operation accuracy of the excavator, and can effectively improve the operation accuracy and efficiency of the autonomous excavator.

show abstract

Section: Control Objectivementioning

confidence: 99%

“…The trajectory of the autonomous excavator is planned based on the needs of the work. 28,29 This section defines the point-to-point trajectory path of the manipulator bucket’s tooth.…”

Section: Tracking Control Strategy and Simulation Analysismentioning

confidence: 99%

Research on the motion control strategy of autonomous excavator based on online compensation

Gong,

Zhang,

Jin

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…On the other hand, YAO et team [24] generated continuous excavation trajectories employing PINN. Furthermore, ZHAO et al [25] also discovered that, under optimally planned trajectories, the fluctuation of excavation angles in unmanned excavators is greater than that observed during manual operations.…”

Section: Introductionmentioning

confidence: 99%

Mining Trajectory Planning of Unmanned Excavator Based on Machine Learning

Jin,

Gong,

Zhao

et al. 2024

Mathematics

View full text Add to dashboard Cite

Trajectory planning plays a crucial role in achieving unmanned excavator operations. The quality of trajectory planning results heavily relies on the level of rules extracted from objects such as scenes and optimization objectives, using traditional theoretical methods. To address this issue, this study focuses on professional operators and employs machine learning methods for job trajectory planning, thereby obtaining planned trajectories which exhibit excellent characteristics similar to those of professional operators. Under typical working conditions, data collection and analysis are conducted on the job trajectories of professional operators, with key points being extracted. Machine learning is then utilized to train models under different parameters in order to obtain the optimal model. To ensure sufficient samples for machine learning training, the bootstrap method is employed to adequately expand the sample size. Compared with the traditional spline curve method, the trajectories generated by machine learning models reduce the maximum speeds of excavator boom arm, dipper stick, bucket, and swing joint by 8.64 deg/s, 10.24 deg/s, 18.33 deg/s, and 1.6 deg/s, respectively; moreover, the error does not exceed 2.99 deg when compared with curves drawn by professional operators; and, finally, the trajectories generated by this model are continuously differentiable without position or velocity discontinuities, and their overall performance surpasses that of those generated by the traditional spline curve method. This paper proposes a trajectory generation method that combines excellent operators with machine learning and establishes a machine learning-based trajectory-planning model that eliminates the need for manually establishing complex rules. It is applicable to motion path planning in various working conditions of unmanned excavators.

show abstract

Model Predictive Control for Unmanned Excavator Based on Skilled Operator’s Operation Trajectory

Jin,

Gong,

Zhao

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Autonomous Excavation Trajectory Generation for Trenching Tasks Based on Skills of Skillful Operator

Cited by 3 publications

References 10 publications

Research on the motion control strategy of autonomous excavator based on online compensation

Research on the motion control strategy of autonomous excavator based on online compensation

Mining Trajectory Planning of Unmanned Excavator Based on Machine Learning

Model Predictive Control for Unmanned Excavator Based on Skilled Operator’s Operation Trajectory

Contact Info

Product

Resources

About