Safety provisions for human/robot interactions using stochastic discrete abstractions

We consider the problem of predicting the probability of an accident in working environments where human operators and robotic manipulators co-operate. We show how, starting from a stochastic discrete time system describing human motion, it is possible to construct a discrete abstraction of the system (a discrete time Markov Chain) to predict the possible trajectories starting from an initial point. The DTMC is used to predict the future evolution for the system, for a fixed horizon, pinpointing the states that, at each step, can be marked as dangerous. This way, the system estimates the probability of an accident and stops the robot when the result is greater than a threshold.