Joint sensor fault detection and recovery based on virtual sensor for walking legged robots

Hashlamon, Iyad; Erbatur, Kemalettin

doi:10.1109/isie.2014.6864786

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…One example would be to use foot contact force [43] or joint position feedback [39], [40] to modulate the trajectory in order to stop lowering a leg if it steps on an obstacle or keep lowering it if stepping into a hole [44]. By having the sensory feedback as extensions and employing a sensor fault detection algorithm like the one presented in [45], it is possible to stop using faulty sensors, thereby preventing them from affecting the trajectory of the original CPG-RBF network. Subsequently, it would then 1 For continuous learning on the real robot, one can use different sensors to calculate the reward function.…”

Section: Discussionmentioning

confidence: 99%

Generic Neural Locomotion Control Framework for Legged Robots

Thor

Kulvičius

Manoonpong

2021

IEEE Trans. Neural Netw. Learning Syst.

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In this paper, we present a generic locomotion control framework for legged robots and a strategy for control policy optimization. The framework is based on neural control and black-box optimization. The neural control combines a central pattern generator (CPG) and a radial basis function (RBF) network to create a CPG-RBF network. The control network acts as a neural basis to produce arbitrary rhythmic trajectories for the joints of robots. The main features of the CPG-RBF network are: 1) it is generic, since it can be applied to legged robots with different morphologies; 2) it has few control parameters, resulting in fast learning; 3) it is scalable, both in terms of policy/trajectory complexity and the number of legs that can be controlled using similar trajectories; 4) it does not rely heavily on sensory feedback to generate locomotion and is thus less prone to sensory faults; and 5) once trained, it is simple, minimal, and intuitive to use and analyze. These features will lead to an easy-to-use framework with fast convergence and the ability to encode complex locomotion control policies. In this work, we show that the framework can successfully be applied to three different simulated legged robots with varying morphologies, and even broken joints, to learn locomotion control policies. We also show that after learning, the control policies can also be successfully transferred to a real-world robot without any modifications. We, furthermore, show the scalability of the framework by implementing it as a central controller for all legs of a robot and as a decentralized controller for individual legs and leg pairs. By investigating the correlation between robot morphology and encoding type, we are able to present a strategy for control policy optimization. Finally, we show how sensory feedback can be integrated into the CPG-RBF network to enable online adaptation.

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Section: Discussionmentioning

confidence: 99%

Generic Neural Locomotion Control Framework for Legged Robots

Thor

Kulvičius

Manoonpong

2021

IEEE Trans. Neural Netw. Learning Syst.

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“…presented in (28)(29)(30), could be used to swap out the faulty control modules. The alternative path or desired walking direction could also be provided by high-level control algorithms, such as the path finding algorithms presented in (31,32).…”

Section: Using All Controllers and Deploying On A Physical Robotmentioning

confidence: 99%

Versatile modular neural locomotion control with fast learning

Thor¹,

Manoonpong²

2021

Preprint

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“…There are many papers considering different problems in the design and application of virtual sensors. Most of these papers are intended to solve different practical applications of virtual sensors: for health monitoring of automotive engines [1]; for active reduction of noise in active control systems [3]; for electronic nose devises [5]; for hiding the fault from the controller point of view [7]; in walking legged robots [8]; for failure diagnosis in aircraft [9]; in the process of fault detection in industrial motor [10]; for fault detection, isolation and data recovery in a bicomponent mixing machine [11]; in humidity sensor systems [14]; in the sensor-cloud platform [17]; for a tunnel furnace [27]. In [16], [18]- [21], [28], virtual sensors are used for fault tolerant control for different types of dynamic systems (descriptor, parameter varying, subject to actuator saturation, etc.).…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensors Design for Nonlinear Dynamic Systems

Zhirabok¹,

Zuev²,

Il³

2023

IJRCS

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The objective of the paper is virtual sensors design, estimating prescribed components of the systems state vector to solve the tasks of fault diagnosis in nonlinear systems. To solve the problem, the method called logic-dynamic approach is used, which allows to solve the problem for systems with non-smooth nonlinearities subjected to external disturbance by methods of linear algebra. According to this method, the problem is solved in three steps: at the first step, the nonlinear term is removed from the system, and the linear model invariant with respect to the disturbance is designed; at the second step, a possibility to take into account the nonlinear term and to estimate the given variable is checked; finally, the transformed nonlinear term is added to the linear model. The relations allowing to design virtual sensor of minimal dimension estimating prescribed component of the state vector of the system are obtained. The main contribution of the present paper is that a procedure to design virtual sensors of minimal dimension for nonlinear systems estimating prescribed components of the state vector is developed. This allows to reduce complexity of the virtual sensors in comparison with known papers where such sensors of full dimension are constructed. Besides, the limitations imposed on the initial system are relaxed that allow to extend a class of systems for which the virtual sensors can be constructed.

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Joint sensor fault detection and recovery based on virtual sensor for walking legged robots

Cited by 13 publications

References 15 publications

Generic Neural Locomotion Control Framework for Legged Robots

Generic Neural Locomotion Control Framework for Legged Robots

Versatile modular neural locomotion control with fast learning

Virtual Sensors Design for Nonlinear Dynamic Systems

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