Fault-tolerant Control of Robot Manipulators with Sensory Faults using Unbiased Active Inference
This addresses fault recovery in robotics, offering a method that simplifies threshold definition without needing extra controllers, though it is incremental as it builds on existing active inference approaches.
The paper tackled fault-tolerant control for robot manipulators with sensory faults by developing a novel active inference formulation that provides unbiased state estimation and probabilistically robust thresholds, achieving validation in a simulated 2-DOF manipulator.
This work presents a novel fault-tolerant control scheme based on active inference. Specifically, a new formulation of active inference which, unlike previous solutions, provides unbiased state estimation and simplifies the definition of probabilistically robust thresholds for fault-tolerant control of robotic systems using the free-energy. The proposed solution makes use of the sensory prediction errors in the free-energy for the generation of residuals and thresholds for fault detection and isolation of sensory faults, and it does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2-DOF manipulator are presented, and future directions to improve the current fault recovery approach are discussed.