Comparative Analysis of Modern Robotic Demining Complexes and Development of an Automated Mission Planning Algorithm ^†

Abstract

This paper presents a comparative analysis of ten state-of-the-art robotic de-mining systems, grouped into (i) sensor-centric platforms for high-precision detection and (ii) rapid mechanical-contact vehicles for clearance. Building on these findings, we propose a lightweight tracked platform (~1.9 T) equipped with a four-channel sensing suite-RGB/IR camera, 32-layer LiDAR, pulsed-induction metal detector, and 2.45 GHz microwave thermography—integrated in an adaptive Bayesian “detect → confirm → neutralize” loop. The modular end-effector permits either pinpoint mechanical intervention or deployment of a linear charge. Modelling indicates an expected detection sensitivity ≥ 95% with a false-positive rate ≤ 5% in humanitarian demining mode and a clearance throughput above 1.5 ha·h⁻¹ in breaching mode. Ongoing work includes CFD analysis of the thermal front, fabrication of a prototype, and performance testing in accordance with IMAS 10.20.

1. Introduction

Mine-explosive barriers remain among the costliest and most hazardous obstacles in modern armed conflicts: According to the United Nations Mine Action Service (UNMAS), 5000–6000 new casualties are recorded each year, and the contaminated area exceeds 110,000 km² [1]. Manual and semi-robotic clearance requires large numbers of explosive ordnance disposal (EOD) specialists, delivers a throughput of only 100–300 m² day⁻¹ per operator, and exposes personnel to direct risk.

The advent of mobile robotic de-mining complexes (RDCs) has opened new possibilities for remote detection and neutralization of mines. The British Armtrac 400/600 machines have demonstrated the ability to work in mixed soil while keeping the operator at a safe standoff distance of up to 1000 m [2]. The combat debut of the Russian “Prokhod-1” system in Donbas confirmed the applicability of heavy milling vehicles under real hostile fire [3].

Parallel research is aimed at improving manipulator kinematic accuracy. An analysis of SCARA mechanisms on a tracked robot showed that kinematic optimization reduced tip chatter by 37% without increasing platform mass [4]; an enhanced sensor–manipulator increased mine-detection depth by 12% while preserving system dimensions [5]; and a chain-decoupled transmission separates load paths between the power and measurement loops, thereby extending joint durability [6].

Significant momentum has also come from incorporating artificial intelligence algorithms for automatic explosive-object recognition and trajectory planning. Early examples include a highly mobile wheeled robot [7], an unmanned quadcopter for minefield reconnaissance [8], and the IGARSS 2021 [9] and SIMS 2021 [10] platforms, which demonstrated the integration of deep learning methods with SLAM navigation.

Additional advantages are provided by multi-sensor suites that fuse infrared, radiofrequency, laser, and ground-penetrating-radar data streams in real time: a magnetometric platform with AI processing achieved < 0.1 m spatial resolution at a depth of 0.5 m [11], while an IoT-enabled robot confirmed the feasibility of low-cost wireless telemetry for local de-mining services [12].

Despite rapid progress, global practice reveals two distinct technological branches: (i) sensor-centric systems that deliver centimetre-level mine localization without disturbing the soil and (ii) mechanical-contact vehicles equipped with flails or ploughs that guarantee detonator destruction but demand significant energy and logistical resources. The absence of a unified efficiency criterion results in empirical equipment selection, and integrating precise detection with assured neutralization remains an unresolved engineering challenge.

2. Materials and Methods

Modern humanitarian de-mining robotic complexes perform remotely controlled detection of explosive ordnance, completely eliminating physical contact between the operator and the hazardous environment. Their hardware–software architecture relies on a multi-layer sensor suite that typically includes pulsed-induction and VLF-induction probes, magnetometers, ground-penetrating radar (GPR), and thermal or hyperspectral cameras [13,14,15]. Sensor data are time-synchronized with navigation and telemetry streams (GNSS/INS, odometer, LiDAR profiling), enabling a quasi-real-time map of subsurface anomalies with a latency below 120 ms [13].

As illustrated in Figure 1, mobile de-mining assets are conventionally grouped into two principal categories:

Category I: Ground autonomous mobile platforms (UGVs). Tracked or wheeled chassis with high off-road mobility carry a sensor “boom” or retractable module that maintains the probe as close to the soil surface as possible (≤10 cm) for precise inductive and radar scanning. The short standoff distance enhances sensitivity to both metallic and dielectric mines, while the on-board computer—running sensor-fusion algorithms and a Kalman filter—instantly localizes targets and generates a risk map [16].

Category II: Elevated remote systems (UAVs or mast-borne). Lightweight unmanned aerial vehicles or telescopic masts carry electro-optical and passive-radiometric sensors, scanning from altitudes of 2–20 m. Their role is wide-area detection of surface and shallow-buried mines via thermal, spectral, and LiDAR contrast, followed by hand-off of coordinates to ground UGVs for point confirmation. The chief advantages are high coverage rates (up to 2 ha h⁻¹) and minimal dynamic loading on explosive devices [17].

Figure 1. Classification of robotic de-mining complexes.

Figure 1. Classification of robotic de-mining complexes.

The analysis includes five robotic platforms that employ mechanical-contact end-effectors to detect and neutralize mines in a single pass or to breach engineered obstacles at speed. The Uran-6 system, driven by a 240-hp diesel engine and fitted with interchangeable rotary flails, clears ground at 2 km h⁻¹, yielding a throughput of roughly 2000 m² h⁻¹ while being remotely controlled from a standoff distance of up to 1 km [18]. These capabilities make it a sought-after asset for infantry support and for clearing built-up areas (Figure 2a).

The DOK-ING MV-4 (Figure 2b) robotic system, powered by a 250-hp Perkins diesel engine and fitted with a chain-flail drum, weighs 6.1 t, negotiates gradients up to 36%, and delivers a clearance rate of 1400–1800 m² h⁻¹ while maintaining a transit speed of 7 km h⁻¹—an essential parameter for operations in rough terrain [19].

The Mine Wolf MW240 (Figure 2c), equipped with interchangeable “flail/plough” tools, achieves up to 3000 m² h⁻¹ at a mechanical penetration depth of 0.25 m; its high specific power (240 hp) and optional autonomous mode make the platform suitable for large-scale humanitarian missions and runway-style strip clearance [20].

For breaching minefields, the U.S. Army employs dedicated charge carriers. The wheeled S-MET (GDLS MUTT) platform carries up to 450 kg of payload, has a 60 km endurance, traverses cross-country at 10 km h⁻¹, and houses a 3 kW generator sufficient to power wire-cutting tools (Figure 2d) [21].

The tracked EMAV by Pratt Miller (mass 3.1 t) deploys a MICLIC linear charge that creates a lane approximately 100 × 5–8 m. Its hybrid powertrain enables a top speed of 70 km h⁻¹ and a “silent” electric drive mode for stealthy approach.

A consolidated assessment of these mechanical-contact systems is presented in

Table 1. Comparative characteristics of robotic platforms.

System	Mass (t)	End-Effector	Clearance Throughput	Control/Standoff (m)	Advantages	Limitations
Uran-6	6.0	rotary flail, chain rake	2000 m2 h−1	remote, ≤1000	Proven in urban clearance; interchangeable tools	High weight; limited detection precision
MV-4	6.1	chain flail	1400–1800 m2 h−1	remote, ≤1500	Compact size; maneuverable in tight terrain	Vulnerable to steep slopes and buried threats
MW240	6.6–10	interchangeable flail/plough	3000 m2 h−1 (depth ≤ 0.25 m)	remote/autonomous, ≤1000	High throughput; autonomous operation	Large logistical footprint; low selectivity
S-MET	1.9	barbed-wire cutter, tow hitch	—(up to 450 kg payload)	remote/semi-autonomous, ≤1000	High modularity; multipurpose use	No built-in detection/neutralization tools
EMAV	3.1	MICLIC linear charge (100 m lane)	≈100 m × 5–8 m per shot	remote/semi-autonomous, ≤1000	Fast breaching; hybrid drive; stealth mode	One-time use per lane; explosive risk

, which summarizes key specifications such as mass, end-effector type, operational throughput, and advantages/limitations for each system.

To benchmark sensor-oriented remote-clearing methods, five representative platforms were selected that span a broad range of passive-detection technologies and autonomy levels. The sample comprises: the multi-agent COMRADE system, which employs a Garrett Infinium LS pulsed-induction metal detector on small scout robots; the fully autonomous Deminer Robot (Figure 3a), which fuses video processing and mapping to build 2-D mine-field layouts; the Wirelessly Controlled Mines Detection Robot, designed for low-cost radio-controlled surveys with video and telemetry transmission (Figure 3b); the highly mobile UGV from the SBIR programmer “Portable Highly Mobile Autonomous Robot for Mine Detection,” where GPS/INS navigation and UWB positioning enhance search accuracy in rough terrain (Figure 3c); and the U.S. Army’s “mine-spotting drones,” which use machine learning algorithms to classify mine types from the air and relay coordinates to ground assets. A multi-factor ranking (sensor modality, autonomy level, mapping resolution, and operational mobility) was applied, revealing clear correlations between sensing technology and the system’s operational value in humanitarian versus military scenarios.

A detailed comparison of these sensor-based systems is provided in

Table 2. Comparative characteristics of sensor-oriented platforms.

System	Sensing Principle	Platform/Propulsion	Autonomy Level	Key Capabilities
COMRADE [13]	PI metal detector + multi-UGV sensor fusion	4-wheel Explorer	Full swarm autonomy	Coverage density ≈ 95% at 0.25 m/s
Deminer Robot [22]	Metal detector + camera + SLAM	Tracked micro-UGV	Full autonomy	Generates GIS-compatible mine maps; avoids detonation (mass < 15 kg)
Wirelessly Controlled Mines Detection Robot [23]	Metal detector + IR rangefinder + wireless camera	4-wheel chassis, RF link	Remote control	Comm. range ≈ 150 m; built-in obstacle avoidance
SBIR Husky [24]	GPS/INS + UWB + laser odometry	6 × 6 UGV, diesel-electric	Semi-autonomous	Positioning accuracy ± 0.05 m; search rate > 1 ha/h
Mine-spotting drones [21]	ML-based EO/IR imagery processing	UAV (rotary-wing/quadcopter)	Semi-autonomous missions	Coverage ≈ 10 ha/h; classifies mine type and burial depth

, highlighting differences in sensing principles, autonomy, and key capabilities [22,23,24].

These findings underscore the critical role of autonomy and sensor integration in enhancing both precision and operational safety during mine-clearance missions. While sensor-based and aerial systems each offer distinct advantages, their limitations highlight the need for more cohesive and adaptive approaches. The potential for hybrid, multi-platform solutions—integrating aerial reconnaissance with ground-level neutralization—marks an important direction for future system development.

To further explore the performance trade-offs and validate the proposed hybrid architecture, a detailed comparative analysis of sensor-centric and mechanical-contact systems was conducted. The following section presents the key results derived from this comparison and discusses the implications for the design of next-generation robotic demining platforms. The findings confirmed that increased levels of autonomy and multi-sensor fusion directly correlate with improved spatial accuracy (<0.5 m) without elevating risk to operators. In contrast, aerial UAVs provide the highest area coverage rates but require ground-based verification for final threat neutralization.

3. Results and Discussion

The comparison of five sensor-based reconnaissance platforms (COMRADE, Deminer Robot, WCMD-Robot, Husky-SBIR, and mine-spotting drones) with five mechanical-contact robotic systems (Uran-6, MV-4, MW 240, S-MET, and EMAV with MICLIC) revealed that current designs fall into two distinct technological branches: the first provides centimetre-level localization without disturbing the ground, while the latter guarantees detonator destruction but operates “blindly.”

To combine the advantages of both approaches, an integrated “detect → confirm → neutralize” loop has been developed, where data from an RGB/IR camera, a 32-layer LiDAR, a pulsed-induction metal detector, and 2.45 GHz microwave thermography are fused within a Bayesian network. The logic of this loop is illustrated in Figure 4.

The data stream begins with multimodal acquisition:

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The LiDAR generates a point cloud of the surface;

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The RGB/IR camera extracts texture and thermal features;

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The PI frame records electromagnetic disturbances from metallic inclusions;

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The microwave horn induces a short-term thermal response, enhancing the contrast of non-metallic casings.

These signals are temporally synchronized via hardware clocking (drift ≤ 50 ms) and passed to a pre-processing module, where ground-plane subtraction (RANSAC), band-pass filtering of PI responses, and thermogram normalization relative to the local background are performed.

In the sensor-fusion stage, feature sets $\left\{F_L, F_V, F_{PI}, F_T\right\}$ are aggregated in a Bayesian network with dynamically weighted reliability. The output is a probability field $P_{tot\left(x\right)}$ with 0.1 × 0.1 m cells. Cells where $P_{tot}\ge 0.55$ proceed to the candidate-generation block: a combined classifier (YOLO-v8-Nano for visual channels + random forest for LiDAR/PI) produces a confidence vector $D\left(x\right)$.

An adaptive thresholding solver compares D(x) to a critical threshold $N_{cr}$, which is automatically adjusted according to the operational scenario:

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0.80 for humanitarian mode (to minimize false-positive rates, FPR);

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0.60 for accelerated military breaching (to maximize throughput).

If $D\left(x\right)< N_{cr},$ the cell is flagged as “uncertain” and re-evaluated following an additional microwave pulse. If $D\left(x\right)\ge N_{cr}$, the neutralization module is triggered. For burial depths ≤ 0.25 m, a micro-flail is deployed; for deeper threats or dense soil, the manipulator places a low-charge linear explosive, remotely initiated. The processed location is logged in a risk map, and the robot plans its next path based on the updated probability field.

The conceptual layout of the lightweight tracked platform, with an approximate mass of 1.9 T, is shown in Figure 5. A rotating mast houses the LiDAR, providing 360° coverage; composite PI frames, each 0.6 m wide, fold out on both sides in their operational position; and a microwave emitter horn is mounted centrally under a Kevlar–aluminum armoured capsule.

The hybrid power system, comprising an 18 kW diesel generator and a 6 kWh LiFePO₄ battery, provides a calculated range of 15–18 km over rough terrain at a specific ground pressure of 27 kPa. Regenerative braking reduces average energy consumption by 12%.

A five-joint manipulator arm, with a 1.2 m reach and 2.5 mm positioning accuracy, is calibrated using AprilTag markers and ensures precise tool placement within the microwave-heated target zone.

The working principle of the combined PI frames and microwave horn is detailed in Figure 6. A brief current pulse induces an eddy magnetic field; if a metallic casing is present, a reflected field is generated and detected by the receiving coil. Simultaneously, the microwave emitter selectively heats a local soil volume, creating a thermal contrast, which is captured by the infrared camera.

This dual-channel approach increases the probability of detecting non-metallic and low-metal mines, as confirmed by electromagnetic and thermal propagation simulations [25].

Preliminary simulations—based on virtual scenarios (ANSYS HFSS for microwave field modelling; MATLAB/Simscape r2022a for kinematic dynamics)—indicate that the proposed architecture can achieve humanitarian-grade detection accuracy, with an expected true positive rate (TPR) of ~95% and a false positive rate (FPR) not exceeding 5%, while in accelerated mode, it could reach a clearance rate of up to $1.5 \mathrm{h}\mathrm{a}·{\mathrm{h}}^{-1}$. These figures will be refined following the fabrication of a full-scale prototype and subsequent field trials in accordance with IMAS 10.20 protocols.

Compared to previously reported systems [5,7,11,25], the proposed architecture demonstrates several novel aspects. While earlier designs focused on either metal detection or visual recognition, our integration of microwave thermography—combined with a pulsed-induction array—offers enhanced sensitivity to both metallic and non-metallic objects. Furthermore, the adaptive Bayesian “detect → confirm → neutralize” loop allows dynamic thresholding based on mission type, which has not been reported in prior studies. These features, supported by simulation data, suggest that our system can outperform legacy robotic platforms in both accuracy (TPR ≥ 95%) and operational safety, especially in variable soil conditions. Unlike traditional platforms that operate in predefined modes, our platform optimizes resource allocation per detection confidence level, contributing to mission efficiency and reduced false alarms.

The most probable limitations of the concept include the following:

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Reduced sensitivity of the pulsed-induction channel in wet, clay-rich soils (water content > 25%).

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Increased power consumption of the microwave module during prolonged continuous operation.

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GNSS navigation degradation under dense vegetation.

To mitigate these, the design includes the following:

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Integration of local UWB positioning.

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An adaptive timing algorithm for microwave pulses to reduce average radiation power.

Taken together, the concept prototype demonstrates the potential of a hybrid approach that combines selective passive detection with guaranteed mechanical–thermal neutralization, forming a foundation for next-generation modular platforms suited equally for humanitarian and high-throughput military demining missions.

4. Conclusions

The proposed conceptual demining platform, weighing approximately 1.9 T, integrates a four-channel sensor architecture-RGB/IR camera, 32-layer LiDAR, pulsed-induction metal detector, and microwave thermography module—within an adaptive Bayesian “detect → confirm → neutralize” loop. This sensor suite enables the adjustment of algorithmic thresholds for two target modes:

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Humanitarian operations (expected TPR ≥ 0.95, FPR ≤ 0.05);

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High-throughput breaching (planned $rate\ge 1.5 \mathrm{h}\mathrm{a}·{\mathrm{h}}^{-1}$), thus minimizing the traditional trade-off between localization accuracy and clearance speed.

The mechanical–thermal end-effector is designed as a modular unit: it can either perform pinpoint mechanical detonator disruption (micro-flail up to 0.25 m) or deploy a linear charge for instant lane creation.

Preliminary estimates, based on simplified digital models of the operational scene, suggest that the system may achieve a sensitivity of approximately 96% with a projected clearance performance of up to 2000 m²·h⁻¹. The additional microwave heating is expected to enhance the detection probability of non-metallic mines without significantly increasing specific energy consumption. These results remain analytical and require empirical validation.

Future research will focus on CFD and thermal modelling of microwave heating in water-saturated soils, fabrication of a full-scale prototype with composite PI frames, and validation tests according to IMAS 10.20 protocols using standard mines (PMA-2/3, PFM-1). Successful completion of these phases will lay the groundwork for the platform’s certification in both humanitarian and military missions.

As such, robotic demining systems remain strategically important for global security: their advancement will reduce the time required to clear contaminated areas, reduce human casualties, and provide the technological foundation for the sustainable reconstruction of post-conflict regions.