ArticleScientific reports2024
Decentralized fault-tolerant control of multi-mobile robot system addressing LiDAR sensor faults.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From Signals to Remaining Useful Life: Multimodal Sensor Fusion for Fault Diagnosis and Prognostics-Methods, Pitfalls, and Reporting Standards.Sensors (Basel, Switzerland) · 2026Review
- Double event-triggered fault tolerant consensus of nonlinear heterogeneous multiagent systems under inconsistent Markov.Scientific reports · 2025Article
- Energy-Aware Sensor Fusion Architecture for Autonomous Channel Robot Navigation in Constrained Environments.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Fault-Tolerant Control of Delta Robots: A Hybrid Optimization Approach for Enhanced Trajectory Tracking.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
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Abstract
The control of multi-robot formations is a crucial aspect in various applications, such as transport, surveillance and monitoring environments. Maintaining robots in a specific formation pose or performing a cooperative task is a significant challenge when a fault occurs among any of the robots. This work presents a Decentralized Fault-Tolerant Control (DFTC) scheme that addresses lidar sensor faults within a system of multiple differential wheeled mobile robots. The robots change the formation shape according to the number of available robots within the formation. A Graph theory is implemented to represent the multi-robot formation and communication. Each mobile robot is equipped with three sensors: a wheel encoder, an Inertial Measurement Unit (IMU), and a lidar sensor. Sensor fault detection and isolation (FDI) is implemented at two levels. The pose estimation obtained from the wheel encoder and IMU is fused using an extended Kalman filter (EKF), and this pose estimation is utilized at the local level of lidar sensor FDI. At the system level, the FDI of the lidar sensor involves computing a residual by comparing the pose estimation with other lidar sensors mounted on other mobile robots within the formation. The presented FTC scheme is simulated in Simulink multi-robot environments.
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