Fault Detection and Diagnosis in Multi-Robot Systems: A Survey.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Robots are increasingly used in diverse applications, necessitating robust fault detection and diagnosis (FDD).
• Traditional FDD methods face challenges in the unique domain of robotics.
• Existing surveys primarily focus on single-robot FDD, leaving a gap in the literature for multi-robot systems (MRS).

Purpose of the Study:

• To provide a comprehensive survey of FDD approaches specifically for MRS.
• To detail the unique challenges posed by MRS attributes to FDD.
• To identify and discuss future research opportunities in FDD for MRS.

Main Methods:

• Literature review of existing FDD techniques applicable to robotics.
• Analysis of MRS characteristics and their impact on FDD.
• Categorization and discussion of FDD approaches tailored for MRS.

Main Results:

• Identification of specific challenges in FDD for MRS, such as coordination and communication complexities.
• Survey of various FDD strategies suitable for MRS, considering their advantages and limitations.
• Outline of key research gaps and potential future directions in the field.

Conclusions:

• FDD for MRS is a critical and evolving research area.
• Addressing MRS-specific challenges is essential for advancing FDD techniques.
• This survey serves as a foundational resource for researchers in FDD for MRS.