Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Control

Lee, Dongjae; Jang, Inkyu; Byun, Jeonghyun; Seo, Ho Seong; Kim, H. Jin

doi:10.1109/iros51168.2021.9635965

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…The obtained matrix is processed to obtain the required parameters m 1 , m 2 , m 3 , l 1 , l 2 , l 3 , and so on. The online gravity compensation torque can be obtained by substituting the parameter identification results into Formula (10).…”

Section: Online Gravity Compensation and Related Parameter Identifica...mentioning

confidence: 99%

“…Controlling the hydraulic cylinder through the known planned path results in the telescopic stroke that satisfies the kinematics theory of the excavator manipulator. [9][10][11] At present, many scholars have studied the control strategy of excavator manipulators. [12][13][14][15][16] In order to realize coordinated control of autonomous excavators, Wang et al 17 employed the cross-coupling pre-compensation (CCP) method to integrate its compensation with the nonlinear proportional integral controller of each actuator.…”

Section: Introductionmentioning

confidence: 99%

“…Controlling the hydraulic cylinder through the known planned path results in the telescopic stroke that satisfies the kinematics theory of the excavator manipulator. 9–11…”

Section: Introductionmentioning

confidence: 99%

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

Gong,

Zhang,

Jin

et al. 2023

Advances in Mechanical Engineering

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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.

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Section: Online Gravity Compensation and Related Parameter Identifica...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gong,

Zhang,

Jin

et al. 2023

Advances in Mechanical Engineering

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show abstract

“…While the computation is quick, the lack of taking into account machine dynamics and external forces resulting from the interaction with the ground makes these approaches incapable of digging in different soils without stalling or being inefficient. Lee et al [5] combined kinematic path optimization with dynamicsaware Model Predictive Control (MPC) to track the kinematic trajectory. Paired with a disturbance observer that estimates external forces, the MPC could track the trajectory accurately in simulation.…”

Section: A Related Workmentioning

confidence: 99%

Soil-Adaptive Excavation Using Reinforcement Learning

Egli

Gaschen

Kerscher

et al. 2022

IEEE Robot. Autom. Lett.

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In this letter, we present an excavation controller for a full-sized hydraulic excavator that can adapt online to different soil characteristics. Soil properties are hard to predict and can vary even within one scoop, which requires a controller that can adapt online to the encountered soil conditions. The objective is to fill the bucket with excavation material while respecting machine limitations to prevent stalling or lifting of the machine. To this end, we train a control policy in simulation using Reinforcement Learning (RL). The soil interactions are modeled based on the Fundamental Equation of Earth-Moving (FEE) with heavily randomized soil parameters to expose the agent to a wide range of different conditions. The agent learns to output joint velocity commands, which can be directly applied to the standard proportional valves of the real machine. We test the controller on a 12-ton excavator in different types of soils. The experiments demonstrate that the controller can adapt online to changing conditions without the explicit knowledge of the soil parameters, solely from proprioceptive observations, which are easily measurable.

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“…In addition, MPC (model predictive control) [22] was also used to track the optimal trajectory. Lee et al [23] used a global planner to generate a reference trajectory that satisfied the maximum bucket volume and minimum force cost, then applied a local planner based on model predictive control to track the reference trajectory. A planning method based on DMP (dynamic movement primitives) that effectively emulated the strategies of experts was developed by Son et al [24].…”

Section: Related Workmentioning

confidence: 99%

Spline-Based Optimal Trajectory Generation for Autonomous Excavator

Zhao

Liu

et al. 2022

Machines

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In this paper, we propose a novel trajectory generation method for autonomous excavator teach-and-plan applications. Rather than controlling the excavator to precisely follow the teaching path, the proposed method transforms the arbitrary slow and jerky trajectory of human excavation into a topologically equivalent path that is guaranteed to be fast, smooth and dynamically feasible. This method optimizes trajectories in both time and jerk aspects. A spline is used to connect these waypoints, which are topologically equivalent to the human teaching path. Then the trajectory is reparametrized to obtain the minimum time-jerk trajectory with the kinodynamic constraints. The optimal time-jerk trajectory generation method is both formulated using nonlinear programming and conducted iteratively. The framework proposed in this paper was integrated into a complete autonomous excavation platform and was validated to achieve aggressive excavation in a field environment.

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Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Control

Cited by 11 publications

References 26 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

Soil-Adaptive Excavation Using Reinforcement Learning

Spline-Based Optimal Trajectory Generation for Autonomous Excavator

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