In extreme environments such as underground mines, petrochemical facilities, transmission towers, landslides, and fire scenes, safety and efficiency are constantly in a state of tension. We have sorted out the application context of artificial intelligence in high-risk positions: with the integration of perception, decision-making, and execution systems, AI is starting from both "reducing exposure" and "predicting risks" to reconstruct standard operating procedures (SOP) for the world's most dangerous jobs. The value of AI is not a mysterious' black box ', but rather to hand over repeatable, dangerous, and extreme actions to machines, while retaining human experience and judgment in a closed loop.
Taking infrastructure and energy scenarios as an example, Skydio in the United States integrates autonomous flight and remote operations into the "DFR (Drone as First Respondent)" and asset inspection plan: after an alarm occurs, the drone arrives at the scene within two minutes to provide real-time footage; In the field of power and utilities, AI obstacle avoidance and vision algorithms enable aircraft to complete close in evidence collection in "high voltage, narrow, and structurally dense" spaces, reducing the reliance on manual operations for climbing, power outages, and containment. Skydio's public information emphasizes that many traditional tasks that require climbing, tying ropes, or entering electrified/confined spaces can now be remotely completed on the ground, significantly reducing the risks of falling, electric shock, and falling objects.
In complex ground spaces and "dark working conditions", Boston Dynamics' quadruped robot uses multi-sensor fusion and autonomous navigation to undertake tasks such as inspection, meter reading, thermal imaging detection, and abnormal acoustic acquisition. RATP, a French public transportation company, has incorporated the robot into its nighttime equipment inspection process for entering areas that are difficult to reach or pose risks of falling, collapsing, and harmful gases, with the aim of "removing people from danger". In addition, cases of continuous process enterprises such as steel and chemical industry also show that robots have formed stable scheduling in high temperature, dust and noise environments.
'Making people stronger' and 'making people farther' are two paths for AI/robots in dangerous positions. The former is represented by exoskeletons and human-machine collaborative equipment. The Guardian XO full body exoskeleton from Sarcos Robotics in the United States has 24 degrees of freedom and can carry an equivalent load of approximately 90 kilograms (200 pounds). The battery can be hot swappable for full shift operation, making it suitable for high repetition, high load but space limited workstations (such as aviation maintenance, loading and unloading of large items in the warehouse, and auxiliary positions in port loading and unloading). Its value is not "showing off strength", but reducing work-related injuries and musculoskeletal diseases from the source, and "converging" the impact of individual differences on safety to the system threshold.
Fire and disaster rescue are another touchstone. The comprehensive review in the academic community shows that the role of ground robots, drones, and AI perception in wildfires, collapses, and toxic environments has shifted from "demonstration" to "contingency planning": the automation level of exploration, monitoring, re ignition identification, structural evaluation, and other processes has rapidly improved, and the cooperation between humans and machines can significantly reduce rescue exposure time and uncertainty. Related studies emphasize the importance of "replacing high-risk actions" while also highlighting the practical constraints of communication, endurance, and cross disciplinary command.
From "action substitution" to "system security", the decision-making and governance value of AI is becoming increasingly prominent. One of the key points of Unite.ai's article is to view AI as the "infrastructure of safety production": triggering intervention before the formation of the accident chain through multi-source data monitoring, predictive maintenance, and abnormal behavior recognition. For example, in the scenario of chemical plants or long-distance pipelines, the thermal, acoustic, and visual data collected by quadruped robots and drones can be compared in real time with health models by algorithms, automatically generate work orders or downgrade strategies, and reduce the cumulative risk caused by "sick operation".
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It is worth emphasizing that 'AI on duty' does not mean 'everything is automated'. The joint case between IBM and Boston Dynamics in the United States shows that the value of inspection tasks lies in "sending information to an interface that people can understand and handle", such as juxtaposing images of suspicious leakage points with inspection history, or overlaying station digital twins with measured data, and leaving it to engineers to determine whether to shut down, reduce load, or send someone for review. This "algorithm first, manual arbitration" process avoids ethical and compliance concerns brought about by "black box decision-making".
From the perspective of landing threshold, the bottleneck that affects the scale of AI in dangerous positions is being resolved by "engineering methodology". Firstly, remote operation and flexible formation. Skydio and other manufacturers have implemented the "one person, multiple machines, cross site remote control" duty mode in DFR, solving the scheduling problem of uneven distribution of alarm situations/obstacles. Secondly, standardize the payload and interface. The quadruped robot platform and exoskeleton are gradually forming standardized sensing, tool, and data interfaces, making it easy to embed into existing CMMS/EAM systems. Thirdly, training and culture. The transition from the role of "operator to system supervisor" requires supporting training and incentive mechanisms in order for frontline teams to truly trust and make good use of AI tools.
We also need to face up to the risk boundary. AI's misidentification, model drift, and communication interruption may lead to misjudgments at critical moments; The "last mile safety" of autonomous systems entering crowded or dynamic environments still requires redundant design and hardware limiting; Data compliance and privacy are equally serious in public safety and industrial settings. In addition, excessive "technology worship" can also weaken basic safety investments, such as traditional but effective systems such as pre shift meetings, work permits, and isolation tags (LOTO). The Unite.ai article reminds that AI should be incorporated into existing security management systems, rather than being a "replacement system".
Looking towards the future, several directions are worth the industry's continuous follow-up. Firstly, the "multimodal AI+mobile platform" will become a universal base for hazardous positions: visual, thermal imaging, gas, acoustic, and vibration data will be integrated at the edge to form three types of on-site capabilities: exploration, inspection, and emergency response, in the form of "task plugins". Secondly, the institutionalization of "human-machine shared responsibility": the authorization, handover, and traceability between machines and humans are written into SOPs, and auditable decision chains are retained in accident investigations. Thirdly, the quantification of "safety economics": re evaluating investment returns based on "exposure time, probability of danger, downtime losses, and insurance premiums", upgrading AI from a cost center to a risk hedging tool. The cross disciplinary research between academia and industry has begun to measure the true safety benefits of AI using indicators such as "replacement exposure hours".
What are the most dangerous occupations?
(1) Mining and underground vehicle operations
Mines/tunnels belong to a composite high-risk environment: dust (occupational diseases such as silicosis), toxic/hypoxic gases, collapse risks, and collision of heavy equipment blind spots coexist. The AI enabled "remote control+unmanned unit" is moving people away from dangerous scenes: multi-source sensors (gas, temperature and humidity, vibration) and cameras are installed underground, and autonomous vehicles/mobile robots are used for continuous inspection. Once the monitoring indicators exceed the limit, evacuation and blockade will be automatically triggered; Autonomous vehicles will standardize high-risk driving processes such as low visibility, repetitive, and heavy loads, significantly reducing the exposure time and misoperation probability of people in narrow tunnels. The key points of the project include: achieving robust positioning/obstacle avoidance under extreme working conditions such as dust, dripping water, and no GNSS; The downgrade strategy of 'losing contact is safe'; And the electrical intrinsic safety design for potential explosive environments.
(2) Long distance commercial truck driving
The risks of long-distance trunk transportation coexist with occupational health pressure: long exposure time, frequent fatigue/night driving, and adverse weather conditions. The practical path is a division of labor model of "autonomous driving on dry road segments+human responsibility for complex road conditions at the end" - covering the vast majority of interstate highways with autonomous driving, compressing driver work time to the "last few miles" of complex scenarios, thereby significantly reducing fatigue related risks. Milestones that can be referenced include: completing the "no safety officer" public road mainline trial run in Arizona, and launching the "no safety officer" commercial transportation in Texas. The difficulties in implementation lie in cross state regulatory consistency, the organization of fleet and loading and unloading connections, redundant design in extreme scenarios, and systematic scheduling of transportation capacity networks.
(3) Maintenance of public utilities and energy facilities
Climbing poles, working around live equipment, and falling from heights are typical high-risk points. Two AI paths are advancing in parallel: one is predictive maintenance (digital twin+multi-source data), which focuses manpower on the "most needed points" and reduces the widespread use of poles/towers; Secondly, airborne/ground robots can replace close range inspections - AI powered LiDAR/imaging can be used for batch inspections of cross regional towers, or maintenance robots moving along the wire can complete close range imaging and defect recognition, while humans can read and issue work orders on the ground. The key challenges include: communication/power supply of long-term assets, wind and rain resistance under adverse weather conditions, and "low missed reporting, traceability" and compliance traceability of defect identification.
(4) High risk medical procedures
The first target of "danger" here is the patient (the direct consequence of surgical failure), and at the same time, high-intensity, long-term, high-precision ergonomic pressure will also feedback to medical safety. Surgical robots+AI assistance are reducing the probability of complications through minimally invasive pathways, jitter suppression, intraoperative navigation, and parallel multimodal perception (imaging/physiological parameters); After surgery, AI can also stratify the risk of complications based on medical records/indicators, helping to develop more reliable monitoring plans. For healthcare professionals, remote surgical intervention/teaching reduces exposure to infection/radiation/fatigue environments; The ergonomic load of long-term surgery also decreases due to the transfer of posture to the robotic arm. The practical constraints focus on algorithm interpretability and responsibility boundaries, data security and ethics, as well as the hard requirements of cross hospital/cross regional network quality for "zero fault tolerance" in remote operations.
(5) Agriculture and Livestock
The high-risk points of agricultural operations come from large machinery, extreme weather/heat injuries, animal impacts, etc. The representative case is the autonomous picking system of "visual+flexible clamping", which can complete the task at a pace of about 7 seconds per fruit, significantly reducing the environmental risks such as sun exposure/heatstroke for frontline workers during the picking season; AI can also extend homework to nighttime or low light conditions, alleviating fatigue caused by seasonal labor shortages. The key to scale implementation is the robustness to "natural variations" (fruit shape, branch and leaf occlusion, ground undulations, mud/dust) and the efficiency of model transfer across seasons/categories.
(6) Underground and enclosed space inspection
The combination of darkness, narrowness, falling, and harmful gas risks is the norm for nighttime inspections of underground transportation/industrial facilities. Four legged robots are included in the nighttime inspection process, entering areas that are difficult to reach or pose risks of falling, collapsing, and harmful gases. They use 360 ° imaging and self-organizing network communication to achieve "proactive inspection and subsequent review", with the goal of "keeping employees away from danger". The joint solution of "mobile perception+edge analysis" shortens the time from discovering anomalies to generating disposable information, thus entering the stage of "fixing the bad before fixing it". This type of case demonstrates that "mobile perception infrastructure" is becoming the underlying capability for managing hazardous environmental safety.
(7) Public Safety and Emergency Response
Drones/robots are becoming the "vanguard" of high-risk positions. The "DFR (Drone as First Sponder)" scheme automatically takes off after an event occurs, with common indicators being takeoff within ten seconds and arrival within about one minute; In power/utility and disaster sites, AI obstacle avoidance and path planning enable aircraft to complete close in evidence collection in "high voltage, narrow, and structurally dense" spaces, reducing the reliance on manual operations for climbing, power outages, and containment. In terms of fire disasters, ground firefighting robots and drones have shifted from "demonstration" to "incorporation into contingency plans", undertaking tasks such as exploration, thermal imaging monitoring, re ignition identification, and structural evaluation. The challenges lie in the vulnerability of disaster communication, the damage to sensors caused by extreme heat flow, and the coordination of cross disciplinary command systems.
(8) Powered Exoskeleton Assisted Jobs
Although not necessarily classified as a "hazardous industry" by traditional statistics, muscle and bone injuries caused by high-intensity physical handling/twisting have long-term hidden dangers. Taking the whole body exoskeleton as an example, with 24 degrees of freedom, a single person can achieve repeated handling of an equivalent load of about 200 pounds (90 kg), equipped with hot swappable batteries to cover the entire shift operation. Its value lies in reducing work-related injuries and musculoskeletal disorders from the source, and "converging" the impact of individual differences on safety within the system threshold. The engineering boundary lies in: not exceeding the physiological joint limits of the human body, anti pinch/speed limit in crowded/dynamic environments, and coordination with on-site safety systems (such as pedestrian priority zones and forklift access).
Cross industry common observation (to make "dangerous work" truly safe, relying on the dual wheel drive of technology and organization)
(A) The priority of 'removing people from the danger zone' is the highest. Whether it's unmanned mining vehicles, tower robots, or DFR and quadruped inspections, the essence is to minimize the accumulated value of "exposure time x risk intensity". Therefore, the system requires fail safe and fault stripping design: automatic degradation upon communication interruption, triggering of "safe parking/evacuation/return" due to abnormal sensing, and manual one click takeover with clear and auditable paths. This' safety engineering foundation 'determines whether automation can truly reduce the chain of accidents, rather than creating new uncertainties.
(B) AI first performs "risk pre positioning" and then performs "automatic closed-loop". Predictive maintenance and digital twin transform "fixing the broken" into "fixing the broken first"; After the defect detection is stable, connect the process of "alarm → work order → dispatch → review" to form a closed loop. The AI LiDAR analysis in energy networks (from tree barriers to hardware defects) and the "mobile edge analysis" in factories for early detection of hotspots/abnormal sounds are both paradigms of "pre intervention": the goal is to intercept accidents from the disaster stage back to the controllable stage.
(C) The division of labor between humans and machines has shifted from "substitution" to "collaboration". In the two high-risk scenarios of trucks and healthcare, the division of labor of "automation covering the plain, humans guarding the mountain pass" is more realistic and easier to pass compliance review: machines are responsible for long-term, repetitive, and dangerous parts, while humans make judgments and provide safety in the complex situation of the last kilometer.
(D) The Three Great Mountains of Landing: Compliance, Engineering Reliability, and Operational Transformation
Compliance: Autonomous driving on the road permit, unmanned aerial vehicle/inspection robot airspace/on-site permit, medical device registration and ethical review, determining the upper limit of commercialization speed;
Engineering: Dust and electromagnetic environment, extreme temperature and humidity/low temperature (cold chain, deep well), wind, rain, and salt spray resistance, which are real tests for sensors and actuators;
Operations: After the change of job profile, the team needs to upgrade from "equipment operator" to "system supervisor/dispatcher/data reader", requiring supporting training and incentives, otherwise the friction of "having a system but no one will use it" will offset the technical dividends. These elements have mature practices to follow in DFR's "one person, multiple machines", quadruped platform's "standardized payload and interface", and factory's "mobile perception+edge analysis" practices.
