|ASKARPOUR MEHRNOOSH||Cycle: XXX |
Section: Computer Science and Engineering
Tutor: BARESI LUCIANO Abstract:
SOFTWARE ENGINEERING FOR ROBOT SAFETY TECHNOLOGY
Traditionally, robots have been assumed to work in structured factory environments or workspaces segregated from human operators by barriers or fencing systems. However, gradually robots are moving into human-populated environments as assistants in different areas such as pallet assembly and maintenance.
More than efficiency for task execution, collaborative robots should provide physical safety for humans. Providentially, there are multiple industrial standards concerning personnel safety, hardware requirements, power and force limiting and injury severity scale, although they focus mainly on hazard prevention and lack an explicit clarification of run-time behavior in the face of an unforeseen event. Thus, in collaboration with ITIA-CNR, we aim to find a coherent solution to the problem of guaranteeing the safety of the operators and the efficiency of the robotic task, by devising exhaustive techniques to discover possible hazards, provide quantitative information about them and identify possible reactions at run-time when there are violations.
We want to design and develop a tool for safety engineers to run a semi-automated risk assessment in a consistent fashion with current standards. The word semi-automated is used because we try to maximize the automaticity as much as possible to reduce human errors in safety super-visioning but still leave some decisions on experience and competence of safety engineers.
In order to do so, we are pursuing to create a logical model of the collaborating system consisting essential primitives of involved parties by means of tools previously developed within the Deepse group for formal verification of properties of real time safety critical systems. In particular, we plan to use the Zot, a bounded Satisfiability checker and the TRIO, a temporal logic language.
In short, the result of our automated analysis can be used by a supervisor to be combined with his/her experience to provide a risk evaluation that is precise, correct and does not overlook unforeseen events.
Advisor: Dino Mandrioli