The prevention of CBRN disasters without risk to human life can only be performed utilizing of unmanned robotic ground vehicles. These robots are particularly important in the reconnaissance phase, since the hazard itself and the extent of the damage are difficult to assess from a safe distance. Unmanned reconnaissance protects the emergency personnel operating the robot outside the hazardous zone.
When the robot has arrived at the site, it is necessary to use the sensors and tools it carries to localize, identify and quantify the hazard. Due to numerous moving compartments, the operation of such robots is extremely complicated and requires initial training and regular exercises. In parallel, the operator must perform decisions based on operational experience under time pressure.
In order to be able to concentrate on the success of the mission at hand, autonomous algorithms offer valuable support in the control implementation of the operator's task. Approach and sampling are two core areas of reconnaissance, which are taken over by newly developed algorithms. Nevertheless, the operator retains decision-making authority, since the targets of both autonomous functions are specified by the operator and the resulting movements can be interrupted at any time. Based on real application scenarios, a tracked robot vehicle is optimized for the intended partial autonomy.
The robot's hardware must also be adapted to the conditions at the target environment. For this purpose, the scope of activities of human emergency personnel in the event of a disaster is considered and the usual measurement devices for substance identification and hand tools for sampling are converted into robot-operated counterparts and integrated.
In the case of nuclear hazards, the robot's focus is localizing the radioactive material. By means of imaging sensors, so-called gamma cameras, the direction of the radiation source is determined in addition to the intensity. The operator is thus able to localize the radiating object and assess its hazard potential.
Raman spectroscopy is a field-proven method for the non-invasive determination of material compositions. Chemical hazards can be identified with these handheld measurement devices, integration into the robot enables its remote control.
In addition, the present project is investigating novel multispectral sensor modalities for their suitability to detect of hazardous substances and, if suitable, integrate them onto the robot.
In order to influence an emergency scenario by sampling, active and passive end effectors for different aggregate states are developed for the arm of the robot. The focus reliable repeatability of the operation enables the autonomous control functions to work consistently without operator intervention.
The developed software and hardware will be integrated on a mobile robot system and tested and optimized in real scenarios on training grounds. The final system will be presented to the public in robotics competitions.