Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, a new kind of hazards being implicated by the robot's capability to manipulate the environment occurs: hazardous environmental object interactions.One of the open questions in safety research is integrating safety knowledge into robotic systems, enabling these systems behaving safety-conscious in hazardous situations. In this paper a safety procedure is described, in which learning of safety knowledge from human demonstration is considered. Within the procedure, a task is demonstrated to the robot, which observes object-to-object relations and labels situational data as commanded by the human. Based on this data, several supervised learning techniques are evaluated used for finally extracting safety knowledge. Results indicate that Decision Trees allow interesting opportunities.
Abstract-Autonomous systems are often needed to perform tasks in complex and dynamic environments. For this class of systems the traditional safety assuring methods are not satisfying because these systems cannot be analyzed completely during development phase. In order to realize a more flexible safety analysis the internal representation of the outside world that is learned by an autonomous Cognitive Technical System is used to identify hazardous situations. The so called safety principles are the hazard knowledge. These can be added to the system prior to operating time without losing the possibility of adjusting or expanding this hazard knowledge during operating time. How these safety principles are generally designed and implemented is explained in this contribution. Furthermore, as underlying Cognitive Technical System provides anticipation capabilities it is possible to expand the planning process in order to take hazard information into account. Finally, in a simulation example is shown how the autonomous system determines possible future actions and evaluates them with regard to hazards in order to provide a plan with acceptable risk.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.