The competitions of rescue have been established since 2000 to foster robot autonomy and an unknown completely and unsettled environment, and to promote the use of robots in high risk areas, for helping human rescue teams in the aftermath of disastrous events. In rescue competitions the task is not to prevent calamitous events but to support operators for people rescue where human accessibility is limited or most probably interdicted. This security topology tasks are crucial when the environment can not be accessed by rescue operators and the aid of robots endowed with good perceptual abilities can help to save the lives of human.
Autonomous robots have to accomplish such a tasks in complete autonomy, which is producing and exploring a map of the environment, recognizing via different perceptual skills the victims, correctly labeling the map with the victim position, and possibly, status and conditions.
DORO cognitive architecture purposely designed for the autonomous finding and exploration tasks required in rescue competitions and focus on hw exploit the ECLiPSe framework in order to implement its model based executive controller.
A model-based role monitoring system is to enhance the system safeness, pro-activity and flexibility. In this approach, the monitoring system is endowed with a declarative representation of the temporal and causal properties of the controlled processes. Given this explicit model, the executive control is provided by a reactive planning engine which harmonizes the mission goals, the reactive activity of the modules functional, and the operator interventions. The execution state of the robot can be compared continuously with a declarative model of the system behavior: the executive controller can track relevant parallel activities integrating them into a global view and time constraint violations and subtle resources can be detected. The system of planning is to compensate these misalignments/failures generating on the fly recovery sequences. Such features are designed and implemented by deploying a paradigm high level agent programming.
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