An Intelligent Collaborative Charging System for Open-Pit Mines

Abstract

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations.

1. Introduction

The use and status of mineral resources. Mineral resources constitute the fundamental material basis for human society, serving as the primary source of industrial supply chains and a critical foundation for industrial development [1,2]. As of the early 21st century, mineral resources accounted for over 95% of China’s energy consumption and more than 80% of its industrial raw material supply. With continuous global population growth and increasing gross domestic product (GDP) across nations, worldwide mineral resource consumption has maintained a steady upward trajectory—a trend expected to persist for decades to come [3,4,5,6]. Currently, amidst the evolution of next-generation information technologies and transformative shifts in the global political landscape, the world is entering a new developmental phase. China, in particular, is navigating unprecedented changes in a century, marked by escalating uncertainties [7,8,9]. Consequently, advancing high-quality development in the mining sector and ensuring timely, demand-driven mineral resource supply are of irreplaceable strategic significance. These efforts are pivotal to safeguarding national security, sustaining economic growth, and maintaining societal stability [10].

The importance of open-pit mining. Currently, mineral resource extraction is mainly carried out separately through open-pit mining and underground mining, with very few mines combining open-pit and underground mining. Statistics show that mineral resources obtained through open-pit mining account for a large proportion of the total mineral development, especially in the field of non-coal mining resources [11,12,13,14]. Data from references [15,16] indicate that the vast majority of China’s building material mines adopt open-pit mining, accounting for approximately 90% of iron ore extraction, 70% of chemical raw material production, and nearly 50% of non-ferrous metal ore exploitation [17,18]. Consequently, investigating efficient and safe open-pit mining methodologies holds significance for advancing high-quality development in the mineral resources sector.

The current situation of open-pit mining technology. At present, except for some soft rock mass mines that use mechanical rock breaking for continuous mining, the rock breaking method in most mines is still mainly rock breaking by blasting. Among them, the open-pit mining process uses numerous methods, including rock drilling and drilling, charging and blasting, loading and transportation, crushing, rock (soil) discharge, etc. The operation mode of each process and the degree of coordination between processes directly affect production efficiency and economic benefits [19,20,21,22]. Up to now, processes such as rock drilling, loading and transportation, crushing, and rock (soil) discharge in the open-pit mining process have basically achieved mechanization and automation. Some modern open-pit mines have taken the lead in achieving intelligent operations [23,24]. However, the technical level of charge blasting and the development of operation equipment are relatively slow. In particular, the development speed of mechanization and automation in the explosive charging process lags behind, and the operation efficiency of explosive charging is relatively low, which seriously restricts the efficient production and economic benefits of open-pit mining [25,26,27,28].

Therefore, researching and exploring an intelligent collaborative and efficient charging system for open-pit mines is of great practical significance for improving the efficiency of explosive charging operations, promoting efficient production in open-pit mines, promoting high-quality development of the mineral resources industry, and advancing the safe, efficient, and intelligent construction and development of open-pit mines.

2. Current Development Status of Key Technologies and Equipment in Open-Pit Mining

2.1. Current Status of Rock Drilling and Blasting Technology and Equipment in Open-Pit Mines

Rock drilling and drilling, charging, and blasting are pivotal technologies in open-pit mining, directly impacting mine production efficiency and operational costs [29]. Bench blasting serves as the predominant method in open-pit mining operations. Among its variants, deep-hole bench blasting is widely adopted due to its compatibility with large-scale mining equipment, feasibility for automation, and high production efficiency [30,31,32,33]. The integration of IoT, artificial intelligence, and photogrammetric technologies (e.g., 3D laser scanning and UAVs) [34] has enabled predictive analysis and quantitative design in deep-hole bench blasting. This advancement drives the maturation of intelligent blasting systems, significantly enhancing the efficiency and automation of open-pit mining operations [35,36].

In recent years, with the rise of intelligent construction in mines, rock drilling and perforation machinery equipment has been continuously upgraded [37]. Currently, the primary equipment for bench drilling in open-pit mining includes down-the-hole (DTH) drills and rotary drill rigs [38]. The literature shows that the development of foreign open-pit drilling equipment came earlier, and the leading suppliers include Atlas Copco, Sandvik, etc. Research and development for rock drilling and perforation equipment in domestic mines started relatively late, but the development speed is fast [39]. Currently, mature ones include XCMG, Sany Heavy Industry, Shanhe Intelligent, etc. Some open-pit mines have achieved localization of rock drilling and perforation equipment [40].

Driven by rapid technological advancements and intelligent manufacturing, rock drilling and perforation equipment is evolving toward intelligent systems. Modern technologies including 3S, 5G communication, IoT, AI, and automatic control have enabled intelligent guidance, precise positioning, and autonomous drilling functions in rock drilling equipment, becoming pivotal technologies for open-pit mine intelligence [41,42]. By integrating satellite positioning and 5G technologies into equipment such as down-the-hole (DTH) drills and rotary blasthole drills, equipped with 3D electronic positioning systems, these devices achieve intelligent guidance and millimeter-level positioning accuracy. This facilitates line-of-sight remote control, teleoperation, and fully autonomous drilling operations. For instance, the Qidashan Iron Mine has developed a digital intelligent drilling system by implementing navigation positioning and information management modules, enabling full-cycle intelligent workflows for rotary blasthole drills, including precise positioning, autonomous hole searching, and high-efficiency drilling [43,44,45,46].

2.2. Current Status of Charging/Blasting Technology and Equipment in Open-Pit Mines

Charging and blasting constitute a critical workflow in open-pit mining operations, involving explosive loading, circuit connection, and detonation processes. Prior to the development of explosive loading trucks, manual filling of explosives and manual circuit initiation were the sole methods for blasting operations. These approaches not only required high labor intensity but also suffered from low efficiency and inconsistent charging quality, directly impacting mine productivity and economic performance. To mechanize the charging process, on-site mixed explosive loading trucks (hereinafter referred to as “explosive trucks”) have been developed and widely implemented. These integrated systems combine explosive raw material transportation, on-site mixing, and automated charging functions, offering significantly higher efficiency and safety compared to manual operations [47]. The adoption of explosive trucks has substantially reduced time and operational costs in blasting workflows. By the end of 2021, China’s permitted production capacity of on-site mixed explosives reached nearly 3 million tons, accounting for nearly 50% of total explosive production capacity, with the market for explosive truck applications experiencing rapid growth [48,49]. However, with the rapid advancement of next-generation information technologies—particularly the emergence of “smart mines” and “smart blasting” concepts—existing explosive truck technologies can no longer fully meet the demands of intelligent mine construction [50].

Currently, two types of explosive loading trucks are employed in open-pit mine charging operations: side-rotating loading trucks and high-mast spiral robotic arm loading trucks. These trucks feature borehole searching and positioning systems to locate blast holes, followed by inserting explosive-filled charging pipes into the holes for completion. Side-rotating trucks, with shorter rotating arms, require frequent back-and-forth travel on the bench platform to complete borehole searching and charging for all holes. High-mast spiral robotic arm trucks, equipped with longer arms, reduce travel frequency and distance on the bench. However, their extended arms lead to difficulties in borehole searching and repeated alignment issues caused by inaccurate hole positioning [51,52].

Field practice has proven that while existing explosive loading trucks have replaced significant manual charging operations, technical challenges persist in the charging process, including inaccurate borehole searching, difficult borehole locating, imprecise blast hole positioning, and prolonged vehicle travel time. These issues often necessitate manual intervention. Current trucks exhibit critical shortcomings such as insufficient collaborative operations, low charging efficiency, and inadequate intelligence, failing to fully achieve automated, intelligent, and highly coordinated charging workflows.

2.3. Current Status of Shoveling, Loading, and Hauling Technologies and Equipment in Open-Pit Mines

Shoveling and hauling constitute two of the core workflows in open-pit mine production. During these operations, the loading and transportation of ore and rock rely critically on mining equipment such as electric shovels, front-end loaders, mining trucks, and belt conveyors [53,54]. Shoveling and hauling involve the coordinated operation of mining equipment within a unified system platform, orchestrated by the production command center to systematically transport ore and rock from the mining face to processing plants (or waste dumps) [55,56,57,58,59,60].

In recent years, shoveling and hauling equipment has undergone continuous upgrades, with operational models progressively transitioning toward automation and intelligence [61].

Intelligent shoveling processes are characterized by precision control of electric shovels and coordinated vehicle–shovel operations. Advanced technologies such as mine sensors and artificial intelligence (AI) enable remote operation and full automation of electric shovels [62,63]. Intelligent hauling processes focus on autonomous mining truck navigation and smart scheduling systems. For example, since initiating its intelligent mining initiative in 2015, the Nansandaozhuang Open-pit Molybdenum Mine has achieved mechanized, automated, and intelligent workflows for drilling, shoveling, and hauling. Its second-generation autonomous trucks now execute orderly platooning across multiple loading points [64,65,66]. Pan Gang’s Zhulan Iron Mine employs an end-to-end intelligent solution integrating “5G private networks + edge computing + vehicle-shovel retrofitting,” establishing remote-controlled electric shovels, autonomous trucks, and collaborative composition of intelligent collaborative charging system modules for production visualization in open-pit mining [67,68].

2.4. Current Status of Mining Robotics and Equipment

In 1993, Xu Shifan [69] proposed that mining robotics would become a pivotal direction for technological advancement in the coal industry. By 2009, Gu Ling et al. [70] reiterated that developing robotic technologies was essential to elevate the scientific and technological capabilities of China’s mining machinery manufacturing sector. Over three decades of development, China’s mining robotics industry has achieved significant progress.

The development of robotics serves as a critical indicator of a nation’s scientific and technological innovation capabilities [71]. With over 40,000 active mines in China, the demand for robotic systems to replace manual operations has intensified due to increasing mining complexity, frequent safety incidents, and sustainability requirements. Mining robotics has now emerged as a pivotal driver in the intelligent transformation of the industry [72].

The deep integration of new-generation information technologies, big data, artificial intelligence, and robotics has ushered in a leapfrogging development window for intelligent mining robotics in China [73,74]. In advancing mining robotics research, scholars have been continuously expanding robotic application scenarios while focusing on critical areas such as precise positioning, path planning, and cooperative control [75,76,77]. In January 2019, the National Mine Safety Administration issued the Key R&D Catalog of Coal Mine Robots, which identified five categories of mining robots: tunneling, coal mining, transportation, safety control, and rescue. With substantial policy support, significant progress has been made in the development of intelligent mining robotics [78]. To date, over 30 types of robots across the five key categories specified in the Key R&D Catalog have been successfully developed and implemented [79]. These include intelligent tunneling systems, automated roof support equipment, smart ventilation control units, robotic inspection devices, and autonomous haulage systems. However, research on explosive loading robots for open-pit blasting operations remains limited. Current on-site explosive mixing and charging trucks still fail to meet the technological requirements for intelligent mine construction. Mining robotics serves as a critical enabler for intelligent mine construction [80]. The sustained development of the mining robotics industry holds significant importance in advancing the safe, efficient, and intelligent high-quality development of mining operations.

2.5. Current Status of Mine Charging Technologies and Equipment

The development of charging technologies and equipment in international mining operations has a long-established history. Leading mining nations such as the United States and Canada pioneered research into on-site explosive mixing and charging systems as early as the 1950s, with companies like American Elec Corporation being among the first to develop bulk explosive loading trucks [81]. Over decades of technological advancement, on-site bulk explosive production has become the dominant method for industrial explosives manufacturing across the Americas, Australia, and Europe. Notably, Australia now utilizes on-site mixing and charging trucks for over 90% of its industrial explosives, where explosives are mixed and loaded directly at mine blast sites [50]. While bulk explosive trucks enjoy widespread adoption internationally, their automation and intelligentization levels remain relatively limited. Representative systems include the U.S.-developed proBlast3 model capable of operational parameter monitoring; Bulk Delivery Trucks that dynamically adjust explosive density through networked geological data analysis; South African systems integrating GPS modules with borehole recognition processors for automated charging control [82]; Australia’s cutting-edge technology that significantly enhances operational efficiency through lithology-adaptive density regulation, delivering substantial productivity gains while reducing operational costs [83].

The development of charging technologies and equipment in China’s mining sector commenced relatively late. Initial technological adoption began in 1986 with the introduction of American bulk explosive loading truck systems, marking the genesis of domestic bulk explosive vehicle technology [84]. A significant technological leap occurred in the early 21st century through systematic innovation, particularly through the integration of various sensors with hydraulic and electronic control systems. These advancements substantially enhanced automation and digitalization capabilities, ultimately driving rapid progress in bulk explosive vehicle technology [85,86,87,88]. Guo Wenxin [89] investigated the overall design methodology for explosive truck loading in open-pit mines, proposing that intelligent mixing–loading vehicles should possess the capability to automatically identify the precise location of each blasthole and achieve accurate explosive charging. Wu Xiaofeng et al. [90] developed a remote control system for production-oriented mixing–loading vehicles which integrates sensors and network controllers to enable intelligent full-process management of transportation and mixing operations through 5G network communication. Liang Feng et al. [91] proposed a wireless remote control approach for PLC systems on mixing–loading vehicles using wireless remote control devices. Tan Weiqi et al. [92] designed an automatic hole aligning explosive loading vehicle by installing hole aligning mechanisms and position sensing devices on a chassis equipped with vehicle positioning systems, thereby realizing automated hole alignment for mixing–loading vehicles.

In recent years, significant progress has been made in the intelligent development and application of explosive charging equipment in mining operations. However, challenges remain in achieving fully unmanned and intelligent systems, particularly in terms of precise perception, accurate control, and efficient explosive charging. Significant gaps remain in achieving fully autonomous and intelligent operations, warranting continued and intensified research efforts.

Research indicates that while mechanization and automation have been realized in drilling, shoveling, and hauling, automation and intelligence in explosive charging and blasting lag significantly, constraining efficient mine development. To improve productivity and realize intelligent operations, advancing automation and intelligence in explosive charging—particularly collaborative and efficient charging workflows—has become the critical challenge for open-pit mining.

3. Modular Composition and Functional Architecture of Intelligent Collaborative Charging Systems in Open-Pit Mines

3.1. System Architecture and Operational Mechanisms of Intelligent Collaborative Charging Methods

From the perspective of hardware architecture, the intelligent collaborative charging system in open-pit mines comprises three key components: bulk explosive delivery vehicles (hereafter referred to as explosive trucks), explosive charging robots, and the coupling mechanism between explosive trucks and explosive charging robots.

A schematic diagram of the structural configuration of the intelligent collaborative charging system in open-pit mines is illustrated in Figure 1.

3.1.1. Structural Configuration and Functional Mechanisms of Explosive Delivery Vehicles

The structural composition of the explosive truck. The explosive truck primarily consists of the following key components: a mixed explosive storage tank, an explosive conveying screw device, an explosive mixing device, and the truck’s walking mechanism.

The main functions of the explosive truck include (1) vehicle positioning and signal transmission/reception; (2) planning the optimal driving route, parking position, and stationary posture based on the blast area scope and borehole collar location data; (3) quantitative calculation and control of explosive charging for boreholes within the blast area; (4) interaction with explosive loading robots; (5) intelligent, efficient, and precise explosive transportation and explosive charging.

A schematic diagram of the explosive truck structure is shown in Figure 2.

3.1.2. The Structure and Functions of the Explosive Charging Robot

The structural configuration of the explosive charging robot comprises several critical subsystems: an autonomous mobility unit for navigation in complex mining environments, a blasthole detection and alignment module with precision positioning capabilities, an orifice-centering mechanism ensuring accurate explosive delivery, a front-mounted explosive charging assembly for direct material deposition, and a rear-mounted interface system for seamless connectivity with explosive delivery trucks through articulated hose couplings.

The explosive charging robot is designed to perform five core functions: (1) positioning and signal transmission/reception, enabling real-time communication with the control system; (2) optimal path planning and autonomous hole detection, which involves calculating the most efficient route based on blast zone parameters, blasthole locations, explosive truck trajectory, and current parking position; (3) precise centering and alignment, ensuring millimeter-level accuracy in positioning the charging nozzle relative to the blasthole orifice; (4) interactive coordination with explosive delivery trucks, including material transfer verification and safety interlock functions; (5) intelligent loading operations, incorporating adaptive flow control and process monitoring to maximize explosive charging efficiency while maintaining safety standards.

A structural schematic diagram of the explosive charging robot is illustrated in Figure 3.

3.1.3. Structural Composition and Functionality of Interface Module Between Explosive Truck and Explosive Charging Robot

Structural composition of the interface module between explosive truck and explosive charging robot: devices and systems.

Device 1: The critical coupling apparatus between the explosive truck and explosive charging robot, consisting of a 360° autonomous rotating structure mounted on the truck roof, a positive-pressure pneumatic conveying system, and an explosive transfer pipeline assembly comprising both rigid and flexible sections.

Device 2: Flexible and detachable linkage structures between components, including an interface and sealing assembly between the explosive truck and rigid explosive transfer pipeline, an interface and sealing assembly between the rigid and flexible explosive transfer pipelines, and an interface and sealing assembly between the flexible explosive pipeline and the explosive charging robot.

System 1: The explosive truck path planning system configured on the vehicle-mounted terminal which performs computational and control functions including route planning for the explosive truck, determination of parking positions, and control of stationary postures.

System 2: The borehole detection and charging path planning system configured on the explosive charging robot which performs computational and control functions including borehole detection route planning, precise centering at the borehole collar, and high-precision/efficient explosive loading operations.

System 3: An interactive system configured between the explosive truck and the explosive charging robot. This refers to the information transmission, command communication, and feedback between the explosive truck and the explosive charging robot, including but not limited to hole-seeking commands, positioning instructions, blast hole opening location data, and the transmission and feedback of charging operation start/stop signals and other operational parameters.

Primary Functions of Explosive Truck–Charging Robot Interface Module. (1) It serves as the critical connection hub between the explosive truck and explosive charging robot, establishing a secure pathway for explosive material transfer; (2) it enables coordinated system control and real-time data exchange to facilitate synchronized operations between both units.

A schematic diagram of the flexible delivery tube structure is shown in Figure 4.

3.2. Technical Components and Interface Devices for Intelligent Cooperative Charging Systems in Open-Pit Mines

3.2.1. Technical Components

The intelligent cooperative charging method features an independent technical system, with its core components encompassing the complete “perception decision execution” chain.

Sensor-based technical components. Serving as the “nerve endings” of the intelligent cooperative charging method, sensor components perform the core functions of environmental perception and status monitoring. This approach employs a multimodal sensor network to achieve comprehensive data collection and transmission throughout the entire process. Common technical components include pressure sensors, fiber-optic deformation sensors, tension sensors, flow meters, and IMUs (Inertial Measurement Units).

Positioning and navigation technical components. Precise positioning forms the foundation for autonomous operations in intelligent charging systems. Modern open-pit mines typically employ an “air–ground” collaborative stereo positioning architecture, with technological evolution demonstrating a clear progression from standalone GPS to multi-source fusion solutions. Common positioning and navigation components include RTK-GNSS systems, LiDAR-based SLAM technology, and visual QR code calibration systems.

Technical components of intelligent algorithms. The intelligent algorithms for smart collaborative charging methods have evolved from traditional PID control to current multi-agent collaborative optimization, with their algorithmic architecture undergoing a paradigm shift from centralized to distributed frameworks. This progression encompasses PID pressure control algorithms, inverse kinematics algorithms, path planning algorithms, and more.

Technical components of collaborative actuation systems. The mechanical actuators of intelligent collaborative charging methods serve as physical carriers for “human–machine–environment” interactions, with their technological evolution characterized by modularity, flexibility, and high reliability. The technical components of collaborative actuation systems include hydraulic locking control, positive-pressure airflow regulation, dynamic pipeline compensation, and emergency braking systems.

In summary, the technical components of intelligent collaborative charging methods span interdisciplinary domains including mining engineering, mechanical engineering, control science, and information technology. Through the synergy of high-precision sensor networks, multimodal positioning technologies, and intelligent algorithms, they enable fully automated and zero-human-intervention intelligent collaborative charging operations in open-pit mines.

3.2.2. Connection Device

The coupling device for intelligent collaborative charging methods in open-pit mines is the key to efficient coordinated operation between explosive trucks and explosive charging robots, primarily involving the effective connection between these two components. This coupling device is an intelligent linkage system designed to connect explosive trucks with charging robots, enabling pneumatic conveying of explosives under positive pressure, dynamic posture adaptation, and high-precision explosive charging. The system primarily consists of a 360° self-rotating structure with a pneumatic conveying unit, rigid and flexible explosive delivery pipelines, and detachable coupling interfaces.

(1)

Materials and manufacturing process of preparation device

The explosive delivery system consists of three functionally integrated components: The rigid pipeline serves as the fixed section, fabricated from high-strength alloy steel through precision cold-drawing and internal wall polishing processes, exhibiting superior structural stability. The flexible pipeline functions as the dynamic section, constructed with multi-layer composite materials via multi-layer winding vulcanization molding technology, enabling bending and telescoping movements. The coupling interface acts as the sealing unit, manufactured from stainless steel using CNC machining combined with laser welding techniques, and incorporates magnetic positioning with hydraulic locking mechanisms to ensure reliable sealing performance.

(2)

Coupling mechanism of linking device

The explosive delivery system employs positive-pressure pneumatic power, comprising a variable-frequency blower and an intelligent air supply system with pressure feedback control. Based on the bending angle α of the flexible pipeline (detected via embedded fiber-optic sensors), the blower dynamically adjusts the air pressure to achieve coordinated pneumatic conveyance. This coupling mechanism ensures complete explosive transfer and charging during the loading process.

The explosive vehicle’s autonomous rotary structure adopts a three-dimensional rotating mechanism, jointly driven by a harmonic reducer and servo motor. This configuration enables 360° dead-angle-free rotation of the rigid explosive delivery pipeline while synchronizing with the RTK positioning/navigation system of the explosive charging robot. Through this integrated design, spatial motion coupling among the explosive vehicle, delivery pipeline, and explosive charging robot is achieved, ensuring coordinated operation in complex terrains.

The rigid–flexible pipeline coupling interface incorporates three core components: a magnetic positioning ring, hydraulic locking sleeve, and self-compensating seal. Its dynamic locking process involves three sequential phases: the magnetic ring guides coarse pipeline alignment, the hydraulic sleeve activates via an electrical signal for intermediate locking, and the seal ring completes ultimate sealing through 0.3 MPa pneumatic expansion. For robot-side coupling, a quick-connect mechanism features a TC4 titanium alloy wedge latch on the explosive charging robot with force sensor feedback for locking status monitoring, and dual-redundant sealing combining a static O-ring with dynamic compensated sealing for enhanced reliability.

4. Technical Implementation Scheme for Intelligent Collaborative Charging in Open-Pit Mines

4.1. Optimal Travel Route Planning and Transportation Allocation for Blasting Trucks

According to the mining design, blasting design, and production plan of the mine, information such as the blasting area range of the open-pit mine, the coordinates of the blast hole orifice, and the charge design of the blast hole is efficiently and accurately transmitted to the on-board terminal of the explosive vehicle. The calculation and control system of the on-board terminal of the explosive vehicle plans the optimal path for the explosive vehicle to transport explosives based on the obtained information, including the driving route and parking position. The explosive vehicle is equipped with flowmeters and solenoid valves. After the explosive vehicle has stopped at the parking point and the loading robot has located the holes, the explosive vehicle compares the position information of the blast holes with the position information of each blast hole in the blasting design database to complete the matching of the blast holes to be loaded and obtain the designed explosive quantity for the blast holes. The explosive supply is opened through the solenoid valve, and the amount of explosive supplied is controlled by the flowmeter, precisely rationed and delivered to the loading robot as needed.

4.2. Optimal Hole-Finding Path Planning for the Loading Robot for Drill Hole Positioning and Loading

According to the blasting design and production plan of the mine, information such as the blasting area range of the open-pit mine, the position of the blast hole opening, and the driving path and parking position of the explosive vehicle is transmitted to the loading robot. The loading robot walks along the planned optimal hole-finding path to the area near the opening of blast hole X (taking blast hole X as an example) and stops. After completing the center positioning of the opening of blast hole X, the front-end charge tube orifice of the charge loading robot is inserted into the center of the orifice of blast hole X, and at the same time, the orifice coordinate information of blast hole X is transmitted to the on-board terminal of the explosive vehicle. After the matching of the charge quantity in the blast hole is completed, a certain amount of the mixed explosive is conveyed to the loading robot through the positive-pressure air of the explosive metering device on the explosive vehicle via the explosive conveying pipeline. The front-end loading pipe of the loading robot loads the mixed explosive into blast hole X, completing the loading work.

4.3. The Explosive Vehicle and the Loading Robot Work Intelligently in Coordination

(1) The explosive vehicle completes the optimal driving route planning for transporting explosives, and the loading robot completes the optimal hole-finding path planning. After the explosive vehicle stops at each parking point, the loading robot conducts hole search, positioning, and loading with the explosive vehicle as the center to ensure that the loading work of all blast holes within the area can be completed. That is, the explosive vehicle and the loading robot, under the optimal driving route and the optimal hole-finding path, aim for the shortest path, the least time, and the most accurate loading, and efficiently complete the loading task of the blast holes in the entire blasting area.

(2) Influencing factors of optimal driving route planning for explosive vehicles. The size range of the explosion zone, the position of the blast hole opening, and the maximum and minimum loading radius of the loading robot (the size range of the mixed loading explosive delivery pipe between the explosive vehicle and the loading robot) influence the optimal driving route plan.

Influencing factors of the optimal hole-finding path planning for loading robots. The size range of the explosion zone, the position of the blast hole opening, the driving route of the explosive vehicle, the parking position and parking posture, and the maximum and minimum charging radius of the charging robot influence the optimal hole-finding path plan.

(3) Collaborative operation between the explosive vehicle and the loading robot. (1) After the explosive vehicle travels and stops along the pre-determined optimal planned route, the instructions are conveyed to the loading robot, which then begins to search for holes and position and load the explosives. (2) After the loading robot automatically locates and precisely positions the holes along the planned hole-finding path, it feeds back the position information of the blast hole opening to the on-board terminal of the explosive vehicle. (3) The explosive vehicle completes the matching of the blast holes based on the position information of the found blast holes, obtains the amount of explosives designed for loading, and conveys the command to the loading robot to start loading and quantitatively dispensing the explosives. (4) After the loading robot completes the loading of the blast hole, it automatically feeds back the completion information of the loading operation to the explosive vehicle. (5) After the loading robot has completed the loading of all the blast holes around the parking position of the explosive vehicle, it follows the explosive vehicle to the next parking position in the explosive area and starts the next cycle of hole searching and loading. (6) The operation is repeated until all the blast holes in the explosion area have been loaded, and the explosive vehicle carrying the charge loading robot then drives out of the explosion area.

5. The Optimal Path Planning Method for Explosive Vehicles and Loading Robots

5.1. An Overview of the Optimal Driving Route for Explosive Vehicles and the Optimal Hole-Finding Route Plan for Loading Robots

The planning of the driving path of the explosive vehicle and the hole-finding route of the loading robot aim to obtain the optimal driving path for the transportation of explosives by the explosive vehicle and the optimal hole-finding route for the search, positioning, and loading of the blast hole by the loading robot by using optimization algorithms. The explosive vehicle and the loading robot work collaboratively and autonomously with the assistance of the connection device and system to complete the loading of the blast hole in the open-pit mine explosive area. The explosive vehicle can complete the loading of all blast holes in the explosion area with the least number of stops and the shortest driving route, as well as the shortest hole-finding route for the loading robot.

According to the design parameters of the explosive vehicle, taking the explosive vehicle as the reference center, the coverage radius of the loading robot is R, and the rotation angle covers 360 degrees. Let us suppose, according to the blasting design scheme, that there are N blast holes in the blasting area of the open-pit mine, and the two-dimensional coordinates are known. All possible parking points for the explosive vehicle in the blasting area are M, and the two-dimensional coordinates are known. The coordinates of the position points where the explosive vehicle enters and leaves the explosive area are L in and L out. When conducting charge loading at blast holes in any blasting area of an open-pit mine, three calculation and optimization tasks need to be completed.

(1)

Calculate the optimal layout of the parking points for explosive vehicles and the corresponding relationship between each parking point and the charge blast holes, reduce the number of parking points, and achieve full coverage of all blast holes at the same time.

(2)

Calculate the shortest path taken by the explosive vehicle. Starting from the known entry point into the explosive zone, it passes through each stop point only once and leaves the known exit point from the explosive zone.

(3)

Calculate the shortest path for the loading robot to travel, starting from the parking point of the explosive vehicle, passing through each blast hole covered by this parking point exactly once, and finally returning to the parking point of the explosive vehicle.

5.2. Optimization Methods and Steps for Obtaining the Optimal Driving Path of Explosive Vehicles and the Optimal Hole-Finding Route Plan of Loading Robots

(1)

The optimal layout of the parking area for explosive vehicles

1. Data input and initialization

The input data are the set of drill hole points P, the candidate set of parking points T, and the coverage radius R of the hole-finding robot.

2. Coverage calculation

By calculating the Euclidean distance between each drill hole point and each parking point, the coverage relationship of each parking point is established. That is, for each parking point, the covered drill hole set Ct is determined, and the parking point and the covered drill hole set are stored using a data dictionary.

3. Optimal selection of parking spots

The parking point optimization problem is transformed into a set coverage problem for solution. From the set of drill holes covered by all parking points, the minimum parking points when covering all drill holes are found through iterative calculation.

4. Output of the coverage relationship

Output the optimal set of parking points T and its corresponding drill hole coverage relationship {tj:C(tj)}.

(2)

Calculation of the shortest path traveled by the explosive vehicle

1. Construction graph (node set and distance matrix)

The entry point L of the explosion area, the exit point L of the explosion area, and the set of the optimal parking points T are combined into a node set to construct a distance matrix, representing the Euclidean distance between the nodes.

2. Shortest path optimization

The shortest path optimization problem is transformed into a Traveling Salesman Problem for solution. Starting from the known entry point L, the path is the shortest when passing through all the parking points in the optimal parking point set and reaching the departure point L.

3. Output of the shortest driving path

Output the node access sequence of the shortest driving path.

(3)

Calculation of the shortest path for the loading robot

1. Construction graph (node set and distance matrix)

For each parking point tj ∈ T, construct the node set, including the parking point and all the drill holes it covers. Calculate the Euclidean distance between nodes and construct the distance matrix.

2. Shortest path optimization

Once again, the shortest path optimization problem is transformed into a travel agent problem solution. Starting from a known parking point, the path is the shortest when passing through all the drill holes that have been covered once and then back to the parking point.

3. Output of the shortest driving path

Output the node access sequence of the shortest driving path.

This study focuses on determining the optimal trajectory for explosive delivery systems within pre-drilled blasting zones, integrating explosive trucks, robotic charging units, and their coupling interfaces. The objectives are to minimize travel distance and operational duration, with key nodes defined as follows: entry point: designated access location near the blasting area (marked in schematic diagrams); exit point: post-charging departure location (marked in schematic diagrams); waypoints: interim stops during transit (red markers in diagrams). The schematic workflow is illustrated in Figure 5.

Path planning for explosive charging robots in borehole localization involves optimizing the trajectory within a pre-drilled blasting zone and integrating the explosive truck’s route plan, robotic charging system, and their coupling apparatus. Key operational nodes (marked in red) designate truck docking points from which the robot radially accesses surrounding blast holes to ensure complete explosive charging coverage while minimizing travel distance. The schematic workflow is illustrated in Figure 6.

6. Simulation Experiments and Result Analysis

To verify the efficiency of the intelligent cooperative charging method, a simulation experiment was conducted based on the blast hole layout of a real open-pit mine blasting design using Python 3.10. The process of traditional charging vehicle operations and the intelligent cooperative charging method were simulated, followed by a comparative analysis of the travel distance and operation time between the two charging approaches.

6.1. Construction of Charging Scenario

The blasting area boundary and borehole coordinates were extracted from the CAD design drawings of a large open-pit iron mine. The coordinates of the boundary points and boreholes were stored in a TXT file for subsequent area modeling and path planning. Within the blasting zone, both traditional charging vehicle operations and the intelligent cooperative charging method were simulated, followed by a comparative analysis of the experimental results. The schematic diagram is detailed in Figure 7.

6.2. Extraction of Parking Positions in Blasting Area

Based on practical experience in open-pit mine blasting operations, charging vehicles typically navigate and park at intermediate positions between adjacent boreholes during loading processes to avoid potential damage to boreholes. Therefore, this study utilizes the midpoint coordinates between neighboring boreholes as candidate positions for vehicle parking. The schematic diagram is detailed in Figure 8, Figure 9 and Figure 10.

The methodology comprises four key steps: (1) data import: the blasting area polygon and borehole coordinates are read from the TXT file; (2) coordinate normalization: the bottom-left vertex (min_x, min_y) of the polygon is calculated, followed by translating all polygon vertices and borehole coordinates to align this point with the origin (0, 0) for subsequent geometric analysis; (3) Delaunay triangulation: a triangular network is constructed from the translated borehole set, with quality control ensuring each triangle’s centroid lies within the blasting area and minimum angles exceed 20° while preserving all edges; (4) parking position generation: the midpoint coordinates of each edge are computed as candidate parking positions, stored in an array with associated edge and borehole endpoint relationships.

6.3. Conventional Loading Scheme for Explosive Loading Trucks

In field operations of open-pit mining, conventional explosive loading trucks typically follow a sequential pattern of borehole rows combined with the nearest-neighbor approach to complete charging tasks, requiring manual assistance throughout the process. For comparative simulation purposes, in this study, we selected the widely adopted bilateral auger loading trucks as representative conventional equipment, with their parking positions precisely located at the midpoint between two adjacent boreholes to enable simultaneous loading operations on both left and right sides. This conventional loading configuration, as illustrated in Figure 11, serves as the baseline for comparison with our proposed cooperative system integrating loading trucks and robotic assistants.

To ensure a fair and objective comparison between the two schemes, identical path planning algorithms were employed for calculating the shortest routes of the explosive charging trucks, thereby enabling a comprehensive evaluation of their operational cycles and efficiency. For the conventional charging truck operation mode, a two-stage strategy combining set covering and path optimization was implemented to generate the minimal set of stopping points covering all blast holes while planning their shortest visiting route. The procedure consists of two sequential steps: (1) stopping point selection using a greedy algorithm based on set covering theory, followed by (2) path planning through a TSP solution incorporating nearest-neighbor initialization and 2-opt local optimization.

After obtaining the minimal set of covering stopping points, an origin point was incorporated as the starting location to formulate a Traveling Salesman Problem (TSP) route. The nearest-neighbor heuristic was first applied to generate an initial path, followed by iterative refinement using the 2-opt algorithm to minimize the total travel distance and reduce path crossings, thereby mitigating excessive steering maneuvers during the explosive charging truck’s operation. This approach effectively simulated the shortest possible working route for the charging vehicle. The schematic diagram is detailed in Figure 12 and Figure 13.

6.4. Intelligent Collaborative Charging Method for Explosive Loading Scheme

The operational workflow of the intelligent cooperative loading system comprises three key stages:

(1)

Parking Position Screening (Coverage Radius + Greedy Set Cover)

The process initiates by inputting the borehole coordinate set, candidate parking positions, and the maximum reachable radius of loading robots. For each parking position, Euclidean distances to all boreholes are computed to identify covered borehole indices within the radius. A greedy algorithm is then applied to iteratively select the position covering the maximum number of uncovered boreholes, with selected positions being stored in a deduplicated array after removing their mapped coverage.

(2)

Explosive Truck Route Optimization

After determining the minimal covering position set, a Traveling Salesman Problem (TSP) is constructed by incorporating an origin point. Consistent with conventional approaches, path planning employs nearest-neighbor heuristics for initial route generation, followed by 2-opt algorithm iterations to minimize total travel distance and eliminate path crossings, thereby simulating the shortest operational route.

(3)

Loading Robot Path Planning

For each selected parking position, its associated uncovered boreholes form a TSP instance. Identical path planning algorithms are executed for all robots to calculate their respective trajectories.

The detailed traveling paths are illustrated in Figure 14, Figure 15 and Figure 16.

6.5. Simulation Results and Analysis

6.5.1. Simulation Methodology and Results

The simulation algorithm was implemented in Python 3.10, utilizing the NumPy library for data import, coordinate array conversion, and computational operations. The Shapely library was employed for polygon rendering and point plotting, while SciPy facilitated Delaunay triangulation and KD-tree construction. Visualization of path trajectories and result outputs was achieved through the Matplotlib (python = 3.10.18, numpy = 2.2.5, shaply = 2.0.6, scipy = 1.15.3, matplotlab = 3.10.0) library.

Based on the selected blasting area of an open-pit iron mine, simulation experi-ments were conducted to obtain comparative results of different charging methods: (1) conventional explosive loading truck results (see Figure 13 in preceding sections), and (2) intelligent cooperative charging method results (see Figure 16 in preceding sec-tions).

6.5.2. Result Analysis

Based on empirical operational parameters—with the explosive truck’s travel speed set at 0.7 m/s (2–3 km/h) and that of loading robots at 3.5 m/s (10–15 km/h)—the simulation results demonstrate significant efficiency improvements. The comparative data of total travel time for loading operations are presented in

Table 1. Comparison of total travel time for explosive loading operations.

Charging Method	Number of Blast Holes	Candidate Parking Points	Actual Parking Points	Explosive Truck Travel Distance (m)	Explosive Truck Travel Time (min)	Charging Robot Travel Distance (m)	Charging Robot Operation Time (min)	Total Charging Travel Time (min)	Time Savings (min)	Efficiency Improvement
Conventional Explosive Truck	231	627	128	1397.71	33.28	0	0	33.28
Intelligent Cooperative Method (Truck + Robot)	231	627	19	692.39	16.48	2260.73	10.77	27.25	6.03	18%

.

From the perspective of operational time analysis, assuming that the hole-searching and charging time of traditional charging trucks is equivalent to that of charging robots, the total travel time (i.e., explosive truck travel time plus charging robot travel time) of the intelligent collaborative charging method during the charging process of the same blasting area is shorter than that of traditional charging trucks, with an 18% improvement in operational efficiency, thereby validating the high efficiency of the intelligent collaborative charging approach. Based on practical field experience in open-pit mines, traditional charging trucks encounter significant difficulties in hole searching, resulting in prolonged hole-searching time and extended charging time per blast hole. In contrast, charging robots exhibit faster and more efficient intelligent hole-searching capabilities, leading to shorter charging time per blast hole. Consequently, the intelligent collaborative charging method demonstrates superior operational efficiency with significantly reduced working time.

From the perspective of travel distance analysis, although the conventional charging truck has a shorter total travel distance, it operates as a heavy-duty vehicle with substantial energy consumption. In contrast, the intelligent cooperative charging method reduces the explosive truck’s travel distance to half that of the conventional approach, while the charging robot covers a longer distance. However, the charging robot is more lightweight and energy-efficient. Consequently, in terms of energy consumption analysis, the intelligent cooperative charging method does not demonstrate significant advantages in energy cost savings, yet it does not incur additional economic costs either.

From an intrinsic safety perspective, conventional charging trucks often require an on-site operator to assist with hole locating and alignment during the charging process, whereas the intelligent cooperative charging method achieves fully automated operation, eliminating one operator per shift. This fundamentally enhances production safety and realizes true intelligent operation.

Considering comprehensive factors such as operational time, energy consumption costs, and intrinsic mine safety, the intelligent cooperative charging method proves more efficient and safer. It represents both the developmental objective and an effective pathway for modern intelligent mining.

7. Conclusions

(1) A new intelligent collaborative loading method for open-pit mines that enables efficient and continuous operation between explosive vehicles and explosive charging robots has been proposed.

For the loading process in open-pit mines, the explosive vehicle and the loading robot work in collaboration, completing the loading efficiently and accurately. The operation equipment components have an interactive function with each other, transmitting operation instructions in real time and feeding back execution information. The automation and intelligence of the explosive charging operation have been promoted.

(2) The on-board terminal module of the explosive vehicle, the loading robot module, and the connection device between the explosive vehicle and the loading robot have been proposed, and the module functions of the explosive vehicle, the loading robot, and their connection devices have been expounded.

The on-board terminal module of the explosive vehicle has functions such as positioning and signal reception and transmission, path planning, and quantitative control of explosives. The explosive charging robot module has functions such as automatic hole finding, precise positioning, and explosive charging. The explosive conveying structure between the explosive vehicle and the loading robot, including the positive-pressure wind power supply device on the top of the explosive vehicle, the 360° rotation device of the explosive conveying pipe, the rigid explosive conveying pipe, the flexible explosive conveying pipe, and related interfaces, is an auxiliary device for explosive transportation.

(3) Two optimal path planning methods have been proposed. The first is the optimal driving path plan for the transportation of explosives by the explosive vehicle, including the traveling route, parking position, and holding posture of the explosive vehicle. The second is the optimal hole-finding route plan of the loading robot, including the hole-finding route of the loading robot, the positioning of the hole opening, and the loading posture.

(4) It has solved the problems of difficult hole finding and inaccurate positioning of the center of the drill hole opening in existing mixed explosive vehicles. The problem of low loading operation efficiency of existing mixed explosive vehicles caused by complex driving paths, frequent turns, and long loading times has been solved.

(5) The intelligent cooperative charging method for open-pit mines fundamentally transforms the operational approach of the charging process, establishing an intelligent collaborative working mode. This advancement narrows the technological gap between charging and other intelligent open-pit mining processes such as drilling and shovel–truck operations, thereby enhancing the overall intelligent level of the entire open-pit mining workflow. Simulation experiments demonstrate that this method improves operational efficiency by 18%, effectively promoting the intelligent construction and development of open-pit mining operations.

(6) Although this study verifies the short operation time and high efficiency of the intelligent cooperative charging method, the experimental design did not specifically simulate the hole alignment time of equipment or compare energy consumption between different devices, which may introduce deviations in the results. Additionally, the simulation experiments were relatively simplistic, and field experiments were challenging to conduct, potentially leading to discrepancies between theory and practice. In future research, the AnyLogic multi-method modeling platform could be employed to develop a hybrid simulation model integrating discrete-event and agent-based modeling techniques. This approach would allow for an accurate reconstruction of the operational workflows of both charging methods, enabling a broader investigation of influencing factors and further validation of the findings.

In the future development of open-pit mining, the industry will inevitably transition from single-point intelligence to system-wide intelligence. The evolution of the intelligent cooperative charging method will focus on the advancement of smart equipment such as charging robots while also imposing new demands on system stability and reliability. Ultimately, this will lead to the realization of both equipment intelligence and system intelligence, achieving the overarching goals of safe, efficient, green, and sustainable mining operations.