Session: ASME Undergraduate Student Design Expo

Paper Number: 176192

Detection of Underground Mines and Explosives 

The threat of underground explosives in conflict zones and post-conflict regions places military personnel, emergency responders, and detection canines in life-threatening situations. Current detection methods depend heavily on human carried sensing devices or trained dogs. The dogs are trained to detect odors emitted from the explosive devices. During such a mine detection operation, the humans or dogs face substantial risks, including injury or death from accidentally triggered explosions, as well as prolonged exposure to hazardous materials and volatile environments. The strain and psychological toll, combined with the risk of a limited lifespan under such conditions, further the need for technological alternatives that reduce reliance on human or animal detection methods, while maintaining current detection capabilities. The purpose of our NH4NO3 (Ammonium Nitrate) Project was to address the need for alternatives by developing a robotic detection system that removed both human handlers and canines from immediate proximity and danger. This would enable explosive detection to occur in a more remote and safe manner. Such a shift in explosive detection methodology should be developed that prioritizes the preservation of life, both human and animal, without compromising the operational effectiveness demanded in threatening environments. The project utilized both chemical sensing and metal detection to form a robust platform capable of identifying improvised explosive devices (IEDs) and landmines, transmitting information to a controlling computer, as well as a connected website for safe recording of the data. By replacing detection teams with remotely operated robotics, the system would allow explosive identification without exposing personnel or canines to blast zones, chemical hazards, or ambush scenarios. The prototype utilized a dual-sensor approach to maximize detection reliability across diverse operational conditions. Underground metallic components, such as those found in IED casings and landmine housings, would be detected using a pulse-induction metal detector with a custom coil designed for various soil compositions, such as soils that would be found throughout the globe and device burial depths. Simultaneously, the chemical sensor array would identify explosive compounds. The initial focus was on vapor detection of ammonium nitrate, an example of a chemical that would be emitted from some explosives devices. The sensor used in the prototype was the MQ-135/MQ-137 ammonia gas sensor. These sensors integrate with signal conditioning circuits that convert the metal detection and chemical concentration measurements into indications of the presence of explosive materials. The multi-sensor integration strategy would enhance detection accuracy across variable terrain conditions, from desert environments to northern zones, potentially providing a more consistent performance regardless of deployment location. The chemical sensing module was testing on various types of soil, chemical concentrations and distances. Testing results supported the ability to detect ammonia gas or metallic devices. The sensors were mounted on an aluminum chassis with a dual-track propulsion system driven by DC motors. The robotic vehicle was remotely controlled. Wireless communication transmitted control information and sensing data to the Raspberry Pi that functioned as the hub computer, which would operate at a safe distance. The early testing showed promise for this system. The development of this technology would provide military and humanitarian organizations with a practical tool that alleviates the risk burden currently carried by detection humans and canines, extends operational capabilities into environments too dangerous for animal deployment, and delivers a sustainable long-term alternative that eliminates the extensive training, care, and rotation cycles required by biological detection methods. This project supports that advanced robotic systems may be able to effectively replace human or canine detection teams.

Presenting Author: Janice Soto Wentworth Institute of Technology

Presenting Author Biography: Undergraduate student in an Electromechanical Engineering program.

Authors:

Douglas Dow Wentworth Institute of Technology
Janice Soto Wentworth Institute of Technology