Autonomous Excavation Research Articles

Development of a Highly Efficient Trajectory Planning Algorithm in Backfilling Task for Autonomous Excavators by Imitation of Experts and Numerical Optimization

Development of a Highly Efficient Trajectory Planning Algorithm in Backfilling Task for Autonomous Excavators by Imitation of Experts and Numerical Optimization

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Data-driven excavation trajectory planning for unmanned mining excavator

In autonomous mining scenarios, excavation trajectory planning plays a significant role since it considerably influences the working performance of the unmanned mining excavator (UME). Aiming at the limited dynamical characterization of traditional theoretical methods that yield unsatisfactory performance in trajectory planning, herein we propose a data-driven excavation trajectory planning framework based on deep learning for improving the operation performance in autonomous excavation scenarios. First, the actual sensing data is collected, and a temporal convolutional recurrent neural network (TCRNN) combining stacked dilated convolutions with an attention-based sequence to sequence (Seq2Seq) module is proposed to predict the digging force accurately. Then, the surrounding material profile is perceived based on Lidar, and a polynomial response surface is performed to point cloud reconstruction. Considering mining efficiency, energy consumption and stability in the excavation process, a multi-objective function is constructed, and a data-driven excavation trajectory planning framework based on TCRNN is established. To obtain the accurate solution of the planning framework, a discretization strategy based on Radau pseudospectral method is developed to model solving to ensure the working performance in autonomous mining. An actual unmanned prototype experiment is performed to investigate the performance of the proposed framework. The results demonstrate the effectiveness and superiority of the proposed framework.

Fractional-order fast terminal back-stepping sliding mode control of autonomous robotic excavators

The vibration of unmanned excavators causes the instability, unsafe operation, and increases fuel consumption. Ground elasticity significantly influences vibrational excitation. Based on modeling of an under-actuated excavator mounted on elastic ground, this article proposes a control system for precisely tracking and suppressing the vibration of autonomous excavators by combining three control approaches: fast terminal sliding mode, back-stepping, and fractional-order control. Such combination leads to a robust controller against matched and unmatched uncertainties, holding flexible fractional gains, rapidly converging with finite-time, and well-rejecting disturbance. Analysis and synthesis of controller rely on fractional calculus, finite-time Lyapunov stability, and Mittag-Leffler theory. In general, the paper has three contributions: (i) setting up an actual system and operational scenario for an elastic ground-mounted excavator, (ii) proposing a fully dynamic model of excavators to support model-oriented control, and (iii) designing a robust fractional-order controller. Validation of control system via simulation and experiment, comprehensive comparison among various methods show the superiority of the proposed approach. Controller well eliminates vibration, tracks accurately, and remains the stability.

Towards Fully Integrated Autonomous Excavation: Autonomous Excavator for Precise Earth Cutting and Onboard Landscape Inspection

Towards Fully Integrated Autonomous Excavation: Autonomous Excavator for Precise Earth Cutting and Onboard Landscape Inspection

Towards Autonomous Excavation Planning

Towards Autonomous Excavation Planning

Design and Control of Autonomous Flying Excavator

This study presents a drone-based excavation platform prototype with the key objectives of balancing stability during excavation, sensing, and digging the soil pile autonomously without human intervention. The whole platform was first designed in CAD software, and then each part of the excavator assembly was 3D printed by using PLA filament. The physical system was then combined with numerous electronic components and linked to various software applications for a drone to perform autonomous excavations. Pixhawk Orange Cube served as the main controller for the drone, while Nvidia Jetson Nano was used for processing data and controlling the tip of the bucket at a specified location for the autonomous excavator. Two scenarios were considered to validate the functionality of the developed platform. In the first scenario, the drone flies independently to a construction site, lands, senses the soil, excavates it, and then travels to another location specified by the mission to deposit the soil.

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

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.

Open InfraBIM for remote and autonomous excavation

Open building information modeling for infrastructure and cloud services featuring the InfraBIM have already been identified as a feasible solution to manage worksite information, from surveys and design to the construction phase. This paper describes the use of InfraBIM on remote and automatically controlled excavators as part of the digitalization of the construction process. The concept of the InfraBIM process is briefly discussed. The systems of guided and remote control are explained, and the Open InfraBIM-based trajectory generation concept for automatic excavation is introduced. To assess the functionality of these systems, a small-scale excavation task was planned and executed using three different modes, with each outcome evaluated and discussed. The quality of trenches dug using the excavator was verified with scanned data, and excavation efficiency was evaluated by measuring the time to complete the task. For remote excavation, the InfraBIM-based guidance was observed to support remote operation better than a video feed. The paper proposes sharing more detailed situational as-built data in the InfraBIM concept to enable adaptive autonomous excavation.

Learning Adaptive Policies for Autonomous Excavation Under Various Soil Conditions by Adversarial Domain Sampling

Learning Adaptive Policies for Autonomous Excavation Under Various Soil Conditions by Adversarial Domain Sampling

Object-level complete coverage path planning for excavators in earthwork construction

Autonomous excavators are gradually gaining attention because they can reduce the waste of human resources and improve the efficiency. This study proposed a complete coverage path planning algorithm for autonomous excavators based on the Rotating Calipers Path Planning (RCPP) algorithm, which called the Excavator-Rotating Calipers Path Planning (E-RCPP) algorithm. This study uses boustrophedon cellular decomposition (BCD) to decompose the construction area to obtain the convex and non-convex sub-areas without obstacles, and describes a non-decomposition principle to determine whether to decompose non-convex areas that are difficult to plan. To obtain the optimal path, an adaptive spacing adjustment model which is used to adjust the spacing between parallel paths is designed. To improve the coverage rate at the corner, this study proposed a novel boundary corner turning method. The algorithm's cost function considers the path length, the number of turns, the coverage rate and the overlap rate. The Digital Orthophoto Map (DOM) of the construction area is created by Unmanned Aerial Vehicle (UAV) and cropped into three polygonal areas, the 2D top-views of the them are used for simulation experiments to verify the performance of E-RCPP algorithm, the results show that the E-RCPP algorithm has better performance when applied to the complete coverage path planning for excavator compared with the traditional RCPP algorithm.

TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite

TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite

Real-time task-oriented continuous digging trajectory planning for excavator arms

Current digging trajectory planning methods for excavator arms are limited to a single digging cycle, which does not meet the continuous excavation demands of the task. To address this issue, a real-time task-oriented continuous digging trajectory planning method for autonomous excavators is presented. The method involves optimizing digging trajectory for a single excavation cycle using multi-objective PSO method, building a PINN model using optimization results as training samples for real-time planning, and embedding the PINN model in planning framework for typical tasks. The method was validated using four different cross-section shapes of the trench. Results show that the average time taken to plan a single digging trajectory is less than 4.5ms, which is negligible compared to the time taken by PSO. The overall performance of the trajectory is only about 5% different from those planned by PSO. This method offers a more efficient and effective solution for continuous excavation tasks.

Research on Excavator Trajectory Control Based on Hybrid Interpolation

In this study, to address the issues of tooth tip operation discontinuity and jitter during autonomous excavator operation, a multi-segment mixed interpolation method utilizing different higher-order polynomials has been proposed. This approach is designed to optimize the tooth tip trajectory of the excavator under multiple constraints, resulting in a smoother trajectory. Specifically, the single-bucket excavator was chosen as the research object, and three different high-order mixed polynomials were utilized to interpolate the trajectory of the digging discrete points. Through a comparative analysis under multiple constraints, this study explored and analyzed the joint angle, angular velocity, and angular acceleration curves of each excavator’s joint. An experimental platform was established to investigate the hydraulic system of an excavator, and the optimal trajectory was controlled using a high-order mixed polynomial interpolation. The results of this study demonstrate that the tracking accuracy of the excavator’s actuator under the optimal interpolation strategy is high, with a maximum displacement deviation of ±3 mm. Additionally, during operation, the excavator manipulator runs smoothly and continuously with minimal flexible impact and vibration.

Excavation pits—progress estimation and cause of delay identification

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web application called “DiBa” to notice a potential delay. If a measurement is smaller or larger than expected a fault identification is done in the DiBa.

Trench visualisation from a semiautonomous excavator with a base grid map using a TOF 2D profilometer

Real-time, three-dimensional (3D) visualisation technology can be used at construction sites to improve the quality of work. A 3D view of the landscape under work can be compared to a target 3D model of the landscape to conveniently show needed excavation tasks to a human excavator operator or to show the progress of an autonomous excavator. The purpose of this study was to demonstrate surface visualisation from measurements taken with a pulsed time-of-flight (TOF) 2D profilometer on-board a semiautonomous excavator. The semiautomatic excavator was implemented by recording the feedback script parameters from the work performed on the excavator by a human driver. 3D visualisation maps based on the triangle mesh technique were generated from the 3D point cloud using measurements of the trenches dug by a human and an autonomous excavator. The accuracy of the 3D maps was evaluated by comparing them to a high-resolution commercial 3D scanner. An analysis of the results shows that the 2D profilometer attached to the excavator can achieve almost the same 3D results as a high-quality on-site static commercial 3D scanner, whilst more easily providing an unobstructed view of the trench during operation (a 3D scanner placed next to a deep trench might not have a full view of the trench). The main technical advantages of our 2D profilometer are its compact size, measurement speed, lack of moving parts, robustness, low-cost technology that enables visualisations from a unique viewpoint on the boom of the excavator, and readiness for real-time control of the excavator’s system. This research is expected to encourage the efficiency of the digging process in the future, as well as to provide a remarkable view of trench work using an excavator as a moving platform to facilitate data visualisation.Graphical abstract

Deep Learning-Based Autonomous Excavation: A Bucket-Trajectory Planning Algorithm

The increased risk to the safety of excavator personnel and difficulty in training them, combined with a manpower shortage, have led to an increased demand for machine automation. This study applies a long short-term memory algorithm for automating a bucket-tip trajectory planning AI system. Unlike other autonomous excavation techniques, the proposed approach in this study performs the bucket-trajectory planning of the excavator without prior knowledge of nonlinear bucket–soil interaction dynamics during excavation, which requires precise adjustment of parameters with the heuristic analysis of correlation between them. Based on data acquisition from excavation of excavator experts, this method uses the three-dimensional point cloud of terrain and bucket motion data in excavation process for training and application of the AI. Especially, we transform the point cloud, which comprises massive number of points and increases the computation complexity, into the much smaller number of values, which are sufficient for representing the three-dimensional shape of target terrain. To ensure safety against collision with underground obstacles, a collision avoidance algorithm is applied to prevent crashes during excavations along the given path, based on continuous monitoring of the pressure in the excavator’s hydraulic cylinder. Comparative experiments reveal that the bucket-tip trajectory planning AI system with the collision avoidance algorithm generates a traceable trajectory for the machine controller, equipped in the excavator, and yields the desired excavation volume and lead time without collision, regardless of the topographic change caused through successive excavations.

Discrete / continuous task bifurcation by frequency separation of human operation for semi–autonomous excavation

Discrete / continuous task bifurcation by frequency separation of human operation for semi–autonomous excavation

Application of Reinforcement Learning to Excavation Path Generation for Autonomous Excavator

Application of Reinforcement Learning to Excavation Path Generation for Autonomous Excavator

Spline-Based Optimal Trajectory Generation for Autonomous Excavator

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.