Robot error detection using an artificial immune system

Biology has produced living creatures that exhibit remarkable fault tolerance. The immune system is one feature that enables this. The acquired immune system learns during the life of the individual to differentiate between self (that which is normally present) and non-self (that which is not normally present). This paper describes an artificial immune system (AIS) that is used as an error detection system and is applied to two different robot based applications; the immunization of a fuzzy controller for a Khepera robot that provides object avoidance and a control module of a BAE Systems RASCAL^TM robot. The AIS learns normal behavior (unsupervised) during a fault free learning period and then identifies all error greater that a preset error sensitivity. The AIS was implemented in software but has the potential to be implemented in hardware. The AIS can be independent to the system under test, just requiring the inputs and outputs. This is not only ideal in terms of common mode and design errors but also offers the potential of a general, off-the-shelf, error detection system; the same AIS was applied to both the applications.