Dynamic Sensor-Tasking and Routing System for Subsurface Hazard Detection

Current field-deployable capabilities for WMD device and component defeat, route reconnaissance, and sensitive site exploitation have made significant strides in rapid surface-level mapping and visual threat triage using unmanned aerial and ground platforms. However, these operational systems encounter a critical bottleneck when confronting subsurface hazards. Visual, optical, and topographic sensors are fundamentally blind to threats that are partially buried, completely subsurface, or obscured beneath soil, blast debris, or disturbed earth. When subsurface geophysical instruments are integrated onto these unmanned platforms to address this blind spot, current field architecture suffers from a severe "collect everything" bottleneck. Lacking localized intelligence, platforms typically execute static, pre-programmed survey scripts which force sensors to operate continuously and indiscriminately across an entire area. This floods constrained tactical communication networks with massive volumes of raw sensor data, overwhelms field computing devices, drastically slows operational tempo, and results in sensor operational blindness when local soil physics degrade or invalidate specific sensor modalities. When conducting standoff reconnaissance of suspected hazard areas—which may contain buried chemical or biological munitions, radiological dispersal device (RDD) components, bulk precursor storage vessels, or associated explosive triggers and conventional ordnance—these bottlenecks force operators into dangerous, prolonged exposure on target. Furthermore, relying on rigid, static classical logic to manage these complex sensor suites in real time introduces a critical limitation, as traditional rule-based programs cannot adapt to highly dynamic, uncharacterized environments where the boundary conditions are not known beforehand. A proactive system is required to answer: "Given this specific environment and mission profile, which sensor should be deployed first, what detection threshold triggers the next modality, and which sensors may be inhibited due to the environment or other physical boundary conditions?" This topic seeks an adaptive, machine-learning-driven sensor-tasking and routing decision engine capable of scheduling sensor deployment and routing platform payloads in real time using probabilistic, data-driven optimization methods. The primary need is an intelligent edge capability that continuously monitors changing site conditions and dynamically queues, activates, or inhibits physical sensor payloads on an autonomous platform. Rather than streaming raw data or running all sensors simultaneously, the system must govern sensor execution through learned decision policies (utilizing techniques such as reinforcement learning, contextual bandits, or partially observable Markov decision processes) that optimize performance based on sensor-inferred environmental physics (such as electrical conductivity and dielectric permittivity), immediate mission constraints (such as required advance speed, standoff distance requirements, and target priority), and real-time sensor performance signatures. When an initial screening modality detects an anomaly, the engine must evaluate the environmental context to dynamically determine if a secondary, higher-fidelity sensor is required to verify and classify the threat, or if the platform should continue its route. By automating this sensor-queuing and routing logic at the edge through explainable and efficient AI, the system will overcome the subsurface bottlenecks of current field suites, eliminate tactical network saturation, minimize time on target, and ensure reliable detection and defeat of subsurface WMD devices and associated hazards across diverse, uncharacterized operational environments. State of Technology: Currently deployed military and commercial technologies have reached a high level of maturity in distinct, stove-piped domains, but they do not integrate these capabilities into a single, closed-loop, in-stride subsurface decision engine. The current technical landscape is characterized by the following: • Standoff Aerial and Surface Visual Mapping Systems: Existing fielded systems utilize unmanned aerial vehicles (UAVs) and ground platforms to perform rapid photogrammetric, optical (EO/IR), and LiDAR mapping of surface terrains. These systems feature mature edge-computing architectures that run real-time automated target recognition (ATR) to classify surface munitions, craters, and obstacles on tactical hand-held tablets in under 30 minutes. However, these systems lack subsurface sensing capability, limiting their ability to detect and characterize hazards that are partially buried, covered by debris, or below the ground surface. • Geophysical Post-Processing Classification Suites: For subsurface detection, advanced electromagnetic induction (EMI) and ground-penetrating radar (GPR) systems are highly mature and accredited for DoD munitions response. These systems utilize sophisticated physics-based inversion models to extract target polarizability tensors and classify anomalies against standard munitions libraries. However, these software suites are strictly designed for post-processing desktop environments. They are not integrated into real-time, in-stride robotic platforms, and they cannot make autonomous decisions in the field regarding active sensor scheduling. • Uncoordinated "Collect-Everything" Sensor Payloads: Current multi-sensor ground platforms operate in a fixed, concurrent acquisition mode where all integrated sensors (such as magnetometers, GPR, and EMI) run continuously regardless of whether the local terrain and environment is physically compatible with the sensor. For example, when a platform transitions into wet, highly conductive clay, a ground-penetrating radar (GPR) antenna suffers from severe signal attenuation, rendering the collected data ineffective for its intended detection purpose. Current systems lack the real-time feedback loops to recognize these environmental changes and limitations then automatically adjust, disable, or redirect sensor payloads. This lack of dynamic management creates a major operational inefficiency, resulting in the collection of highly degraded data that requires excessive post-processing, while unnecessarily consuming onboard battery power, communication bandwidth, and valuable mission time. • Static Autonomous Path Planning: Existing autonomous ground and aerial navigation algorithms plan paths based on surface traversability, geometric obstacle avoidance, and pre-mapped terrain features. They do not ingest real-time subsurface hazard maps or dynamically adjust their routing, payload dwell times, or sensor modalities based on changing subsurface soil electromagnetic properties or detected anomalies. • Adaptive AI/ML for Sequential Decision-Making and Path Planning: In adjacent fields such as autonomous warehouse robotics, dynamic network routing, and space domain awareness, machine learning is highly mature for solving complex, real-time sensor scheduling and path-planning challenges. State-of-the-art approaches utilize Deep Reinforcement Learning (DRL) and Contextual Multi-Armed Bandits to train autonomous agents that make optimal sequential decisions under extreme uncertainty. These AI models observe dynamic environmental contexts (e.g., localized obstacle density or changing wireless signal-to-noise ratios), weigh the trade-offs between exploring unknown areas and exploiting known paths, and automatically adjust sensor power cycles or update the platform’s route on-the-fly. However, these advanced decision-making frameworks have not yet been integrated with or trained on subsurface geophysical physics, leaving a critical gap where current autonomous AI agents remain incapable of managing multi-modal subsurface sensor suites.