Faulty control system

The integration of robotics into everyday life is increasing and these complex systems are exposed to complex faults that require rapid identification for seamless repair and continuous operation. These faults have a complex impact on cognitive aspects such as perception, decision-making and behavioral execution in robots. Robotic fault detection and diagnosis research (FDD) focuses primarily on individual robot scenarios, which lack a comprehensive investigation in multi-robot systems (MRSs). Our paper introduces a robotic control method to control operations in a wide range of production systems. The control system architecture developed by multiple robots provides a local and global cognitive system that is shared between them. Internal dynamics, represented by finite state machines, represent different operating scenarios. The rigorous formal methodology such as Petri Nets and Computer Tree Logic (CTL) validates the accuracy of control architectures and fault management strategies. Building a model of trust based on the historical interactions between intelligent robots facilitates the creation of a global cognitive system that enables adaptation in the management of errors. Our research is launching a trust estimation model, especially the collaboration between reliable robots, and increasing the fault flexibility of multirobot control systems. The contributions include the design of multi-robot control architectures, the management of failures of control robots, and the formulation of trust models.