Work

Integrating Convolutional Neural Networks with Finite-State Machines for Fault Detection in Mobile Robots

Year: 2026

Type: article

Abstract: This paper highlights a communal fault detection and isolation framework integrating a convolutional neural network (CNN) with a finite-state machine (FSM). The proposed concepts ensure state-based controlled discriminate pattern recognition and enable the diagnosis of different anomalies in the mobile robot in a multi-robot environment. The framework processes the time-series sensor data through the convolution layer upon experiencing different types of fault and governs different states based on fault diagnosis and recovery. The proposed concept has been validated using a Python 3.11 and Webot environment featuring the shrimp robot in a multi-robot arena. The model obtained an accuracy of 97% in identifying and classifying faults, enabling automated recovery of faulty robots in the multi-robot environment. Experiments conducted on different simulators demonstrate that effective fault management can be achieved with low training loss.

Source: Robotics

DOI: 10.3390/robotics15030061

Authors: Nilachakra Dash, Bandita Sahu, Kakita Murali Gopal, Indrajeet Kumar, Ramesh Kumar Sahoo

Institutions: GIET University, Birla Global University

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

OpenAlex ID: W7136170231

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