Description:

The pilot will take place at the Integral Security and Emergency Training Centre (CIFSE) in Madrid, covering 15,000 square meters with 10,000 square meters of constructed area. The scenario involves a truck carrying explosive chemicals that collides with another vehicle under a bridge, resulting in toxic smoke and multiple cars involved. Given the hazardous materials, responders deploy smart unmanned ground vehicles (UGVs), like NEO HD and Anymal, to assess the spread of toxic materials and rescue victims. The UGVs autonomously map the area and relay the information to remote operators, aiding decision-making.

A toxic leak is detected on the truck, and a robotic arm on NEO HD neutralizes it. A smart UGV later finds a trapped victim, assisting in their evacuation. Another UGV (TEC800) helps extract hazardous materials during the operation.

Motivation:

In CBRN-E (Chemical, Biological, Radiological, Nuclear, and Explosive) incidents, first responders face significant risks, including the need to detect and identify hazardous materials, determine their spread, and neutralize them while ensuring the safety of affected populations. These dangerous tasks often require responders to work in close proximity to the hazard, putting their lives at risk. Smart autonomous unmanned ground vehicles (UGVs) can greatly aid in such operations by scouting environments, identifying and assessing hazardous materials, and remotely manipulating dangerous substances with robotic arms. Additionally, UGVs can carry heavy equipment, evacuate toxic materials, and provide support in victim evacuation, helping reduce the danger for human responders.

Expectations:

• Safer operation with less human engagement for the assessment of the contaminated area.
• Safer operation no human engagement in the manipulation of the toxic leak.
• Accurate identification of the contaminated area.
• Accurate detection and localisation of victims.
• Improved efficient response reducing time taken to assess the area, leading to quicker and more effective responses and evacuation of victims.

Description:

A strong earthquake hits Athens. The action takes place in HRTA’s Afidnes Training Center located 20km from the city, across ~5 hectares with abandoned buildings, auxiliary structures and underground tanks made from reinforced concrete, a debris field for confined space training and UGV testing ground, plus plenty of open-space areas with a dry riverbank, a large tunnel underneath the avenue and two vertical shafts (wells).

Motivation:

The current USAR priorities during operational deployment are focused around three main items: 1) the first responder: safety, monitoring, navigation, et 2) the victim: Detection, location, support, medevac assistance, etc. 3) Situational awareness: inside the FR team, between FR team and command and control centre, etc. The priority regarding risks is to put distance between the FR and any potential hazard (i.e., remote sensing). Having a biderectional information flow between the FRs and the command and control is crucial to collect first- hand live information from the worksite and therefore foster informed decision making by the commanders when is comes to the deployment of assets like UXVs. The potential to enhance the speed, accuracy or safety of one or more of these aspects is an open topic for novelty addressed in CARMA.

Expectations:

• Safer operation with less human engagement (remote route identification, hazards identification, transport and deployment of equipment).
• Faster detection and localisation of victims and dialogue with victims.

Description:

A fire breaks in the “Meyerbeer-Opéra” underground car park in the centre of Paris. This site is challenging because of its 8 underground levels including commercial activities on the -1 level. Due to its great depth, it is representative of a parking which generates difficulties during intervention (loss of communication, large recognition space, etc.).

Motivation:

Underground car park fires in Paris are a frequent and resource-intensive challenge, occurring several times a week and requiring significant response efforts—often involving at least 15 fire engines and around 50 firefighters. These incidents pose multiple risks for responders, including extreme heat (“oven” effect), zero visibility due to smoke, challenging site layouts, rapid horizontal fire spread from vehicle to vehicle every 10 minutes, and, in critical cases, risks of structural subsidence and collapse. The primary operational goal is to quickly contain the fire to limit its spread and reduce impact, ensuring firefighter safety and enabling victim rescues.

Expectations:

• Capacity to scout a multi-floor underground environment with very poor visibility.
• Make reconnaissance time at least twice faster and possible even in very low visibility environments.
• Capacity to identify and locate a firefighters in distress or victim.
• Path marking.
• Reduction of firefighters’ vulnerability time by a factor of at least two (less human exposition to hazard).

Description:

At Marseille, the largest French port and a key Mediterranean hub, a fire breaks out near docks storing hazardous materials, including batteries and chemicals. The fire threatens to spread to storage sheds and potentially cause explosions, endangering the urban area with a toxic plume. The Fire and Rescue Service of Marseille (BMPM) responds swiftly, deploying robots (Anymal, NEO HD) to locate and neutralize the fire while others (TEC800) focus on protecting the storage sheds. The robots provide valuable remote assessments, allowing firefighters to safely approach the scene, guided by the robots and supported by additional equipment for hazard neutralization.

Motivation:

Incidents onboard ships are particularly challenging due to the difficulty in navigating the vessel’s design, which complicates fire-fighting efforts. The rapid spread of heat and smoke, exacerbated by the ship’s metal construction, poses a significant risk. Additionally, fires on ships docked at quays can quickly spread to other vessels, potentially affecting the harbour infrastructure and escalating the crisis, which could have serious implications for surrounding populations.

Expectations:

• Onboard scouting and assessment made at least three times faster (also due to the possibility intervene sooner than human responders).
• Detection of victims/localization in fire sources in less than 40 min.
• Extraction of first victims in less than 1 hour.
• Significant reduction of the exposure of FRs to the hazards thus reducing risks to the rescue team.