Search Results (39)

This work explores the application of robotic systems in archaeology, highlighting their transformative role in excavation, documentation, and the preservation of cultural heritage. By combining technologies such as LiDAR, GIS, 3D modeling, sonar, and other sensors with autonomous and semi-autonomous platforms, archaeologists can now reach inaccessible sites, automate artifact analysis, and reconstruct fragmented remains with greater precision. The study provides a systematic overview of underwater, aerial, terrestrial, and other robotic systems, drawing on scientific literature that showcases their innovative use in both fieldwork and museum settings. Selected examples illustrate how robotics is being applied to solve key archaeological challenges in new and effective ways. While the paper emphasizes the potential of these technologies, it also addresses their technical, economic, and ethical limitations, concluding that successful adoption depends on interdisciplinary collaboration, careful implementation, and a balanced respect for cultural integrity. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces and incomplete data in real-time, leading to significant challenges in practical deployment. To address these gaps, we present a novel framework integrating curve approximation, surface reconstruction, and marching cubes algorithm to transform raw sensor data into a detailed and computationally efficient soil surface representation. Our approach improves site modeling accuracy, paving the way for reliable and efficient construction automation. This paper enhances sensory data quality using surface reconstruction techniques, followed by the marching cubes algorithm to generate an accurate and flexible 3D soil model. This model facilitates rapid estimation of soil volume, weight, and shape, offering an efficient approach for environmental analysis and decision-making in automated systems. Experimental validation demonstrated the effectiveness of the proposed method, achieving relative errors of 4.92% and 1.42% across two excavation cycles. Additionally, the marching cubes algorithm completed volume estimation in just 0.05 s, confirming the approach’s accuracy and suitability for real-time applications in dynamic construction environments. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

In this article, the current state of the art in the area of autonomously working and mobile robots used for inspections in deep underground mining and exploration is described, and directions for future development are highlighted. The increasing demand for CRMs (critical raw materials) and deeper excavations pose a higher risk for people and require new solutions in the maintenance and inspection of both underground machines and excavations. Mitigation of risks and a reduction in accidents (fatal, serious and light) may be achieved by the implementation of mobile or partly autonomous solutions such as drones for exploration, robots for exploration or initial excavation, etc. This study examines various types of mobile unmanned robots such as ANYmal on legs, robots on a tracked chassis, or flying drones. The main scope of this review is the evaluation of the effectiveness and technological advancement in the aspect of improving safety and efficiency in deep underground and abandoned mines. Notable possibilities are multi-sensor systems or cooperative behaviors in systems which involve many robots. This study also highlights the challenges and difficulties of working and navigating (in an environment where we cannot use GNSS or GPS systems) in deep underground mines. Mobile inspection robots have a major role in transforming underground operations; nevertheless, there are still aspects that need to be developed. Further improvement might focus on increasing autonomy, improving sensor technology, and the integration of robots with existing mining infrastructure. This might lead to safer and more efficient extraction and the SmartMine of the future. Full article

To realize the autonomous operation of unmanned excavators, this study takes the four-axis manipulator arm of an unmanned excavator as the research object, uses the five-order B-spline curve for operation trajectory planning, and proposes an improved particle swarm optimization algorithm for the continuous trajectory optimization problem of excavator single operation. The specific contents are as follows: based on the standard PSO algorithm, dynamic parameter update is used to enhance the global search ability in the early stage and improve the local search accuracy in the later stage; the diversity monitoring mechanism is enhanced to avoid premature maturity convergence; multi-particle SA perturbation is introduced, and the new solution is accepted according to the Metropolis criterion to enhance global search ability. The adaptive cooling rate flexibly responds to different search situations and improves the search efficiency and quality of the solution. To verify the effectiveness of the improved PSO–SA algorithm, this study compares it with the standard PSO algorithm, the standard PSO–SA algorithm, and the MPSO algorithm. The simulation results show that the improved PSO–SA algorithm can converge to the global optimal solution more quickly, has the shortest time in trajectory planning, and the generated trajectory has higher tracking accuracy, which ensures that the vibration and impact of the manipulator during motion are effectively suppressed. Full article

Artificial intelligence in the mining industry is key to improving safety, optimizing resources, and ensuring sustainable operations in complex environments. The main objective of this research is to develop an autonomous mobile station equipped with artificial vision and artificial intelligence to identify and track equipment, people, and animals in critical areas of mining operations, issuing real-time alerts to reduce occupational risks and improve operational control. The research is applied with an experimental approach, designed to validate the effectiveness of the proposed system in real open-pit mining environments. The proposed methodology consisted of five stages: (i) Selection of data collection equipment, (ii) Definition of the positioning scheme, (iii) Incorporation of the communication system, (iv) Data processing and transformation, and (v) Equipment identification and tracking. The results showed an average accuracy of 98% in the validation and 95% in the test, achieving perfect performance (100%) in key categories such as excavators and drills, highlighting the potential of this technology to transform mining towards safer and more efficient standards. Full article

As a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position tracking control spawned from external disturbance and other factors in the self-mining servo system of excavators, a strategy of sliding mode backstepping control based on the particle swarm optimization algorithm and neural network disturbance observer (PSO-NNDO-SMBC) was recommended accordingly. Meanwhile, the complex disturbance was estimated online and compensated for by the system control input by the universal approximation property of the neural network disturbance observer (NNDO). Afterwards, the uncertainty of control parameters was optimized by the particle swarm optimization algorithm (PSO) and was fed back to the controller parameter input end. Afterwards, a co-simulation model of MATLAB/Simulink (MATLAB2023b) and AMESim (Simcenter Amesim 2304) was established for simulation analysis, and a test bench was set up for comparison and verification. As proven by the experimental results, PSO-NNDO-SMBC possessed strong anti-interference ability. In contrast to the sliding mode backstepping control based on the particle swarm optimization algorithm (PSO-SMBC), the maximum displacement tracking error was lowered by 50.5%. Furthermore, in comparison with the Proportional-Integral-Derivative (PID), the maximum displacement tracking error was decreased by 75.2%, which tremendously optimized the control accuracy of excavator bucket displacement tracking. Full article

The underground plastic vertical drains (PVDs) are a significant problem for shield machines in tunneling construction. At present, the main method to deal with PVDs is to manually adjust the parameters of the shield machine. To ensure that a shield machine autonomously recognizes and adjusts the control in soil layers containing PVDs, this study constructs a shield machine advance and rotation state-space model utilizing Bayesian decision theory for the judgment of excavation conditions. A Bayesian model predictive control (Bayes-MPC) method for the shield machine is proposed, followed by a simulation analysis. Finally, a validation experiment is conducted based on a Singapore subway project. Compared with traditional methods, the method proposed in this paper has better performance in the simulation, and it also has demonstrated effectiveness and accuracy in experiments. The research outcomes can provide a reference for the adaptive assistance system of shield machines excavating underground obstacles. Full article

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges, such as the need for sophisticated decision making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field. Full article

Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning. Full article

Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data. Full article

In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer’s accuracy (PA) over user’s accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring. Full article

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. Full article

The accurate calculation of ore rock fragmentation is important for achieving the autonomous mining operation of mine excavators. However, a single mode cannot accurately calculate the ore rock fragmentation due to the low resolution of the point cloud mode and the lack of spatial position information of the image mode. To solve this problem, we propose an ore rock fragmentation calculation method (ORFCM) based on the multi-modal fusion of point clouds and images. The ORFCM makes full use of the advantages of multi-modal data, including the fine-grained object segmentation of images and spatial location information of point clouds. To solve the problem of image under-segmentation, we propose a multiscale adaptive edge-detection method based on an innovative standard deviation map to enhance the weak edges. Furthermore, an improved marked watershed segmentation algorithm is proposed to solve the problem of low segmentation accuracy caused by excessive noise of the gradient map and weak edges submerged. Experiments demonstrate that ORFCM can accurately calculate ore rock fragmentation in the local excavation area without relying on external markers for pixel calibration. The average error of the equivalent diameter of ore rock blocks is 0.66 cm, the average error of the elliptical long diameter is 1.42 cm, and the average error of the elliptical short diameter is 1.06 cm, which can effectively meet practical engineering needs. Full article