Activation Mechanism in Robots

Abstract

In robotics, implementing strategies based on human body would help a robot to handle situations in a hostile environment where human intervention is not possible. Multiple robots may work in a coordinated manner to achieve certain tasks. One of the big problems is detection and recovery from failures since human intervention may not be possible. To this end we propose an autonomic self-detection and self-recovery robotics architecture based on the human immune system. In this thesis, we look at self-detection and self-recovery of communications failure. In particular, we look at two types of communication failures; failures caused by robot isolation and failures caused by intermittent message loss. This thesis focuses on one component of the autonomic robotic architecture, namely, the activation mechanism in robots which makes the robot respond to the communication failure that it had encountered during its operation by sending some suggested action. This is similar to the work done by the thymus and bone marrow (cell mature stations) in the human immune system. The activation unit helps in storing, learning through experience and using the experience for future problems. It also learns through the feedback sent by another unit and uses the experience for future problems. Simulation results show that the proposed architecture helps in minimizing failures by providing more probable actions to make the robot act dynamically.