Safety is one of the key issues in the use of robots, especially when human–robot interaction is targeted. Although unforeseen environment situations, such as collisions or unexpected user interaction, can be handled with specially... more
Safety is one of the key issues in the use of
robots, especially when human–robot interaction is targeted.
Although unforeseen environment situations, such as collisions
or unexpected user interaction, can be handled with specially
tailored control algorithms, hard- or software failures typically
lead to situations where too large torques are controlled, which
cause an emergency state: hitting an end stop, exceeding
a torque, and so on—which often halts the robot when it
is too late. No sufficiently fast and reliable methods exist
which can early detect faults in the abundance of sensor and
controller data. This is especially difficult since, in most cases,
no anomaly data are available. In this paper we introduce a new
robot anomaly detection system (RADS) which can cope with
abundant data in which no or very little anomaly information
is present.
robots, especially when human–robot interaction is targeted.
Although unforeseen environment situations, such as collisions
or unexpected user interaction, can be handled with specially
tailored control algorithms, hard- or software failures typically
lead to situations where too large torques are controlled, which
cause an emergency state: hitting an end stop, exceeding
a torque, and so on—which often halts the robot when it
is too late. No sufficiently fast and reliable methods exist
which can early detect faults in the abundance of sensor and
controller data. This is especially difficult since, in most cases,
no anomaly data are available. In this paper we introduce a new
robot anomaly detection system (RADS) which can cope with
abundant data in which no or very little anomaly information
is present.
