An Evolutionary Method for Improving the Reliability of Safetycritical Robots against Soft Errors



Nowadays, Robots account for most part of our lives in such a way that it is impossible for us
to do many of affairs without them. Increasingly, the application of robots is developing fast
and their functions become more sensitive and complex. One of the important requirements of
Robot use is a reliable software operation. For enhancement of reliability, it is a necessity to
design the fault tolerance system. In this paper, we will present a genetic algorithm and
learning automata with high reliability to evaluate the software designed into the robot
against soft-error with minimum performance over-head. This method relies on experiment;
hence, we use the program sets as criteria in evaluation stages. Indeed, we have used the error
injection method in the execution of experimental processes. Relevant data, regarding
program execution behavior were collected and then analyzed. To evaluate the behavior of
program, errors entered using the simple scalar simulation software.


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