A general motion control architecture for an autonomous underwater vehicle with actuator faults and unknown disturbances through deep reinforcement learning

Huang, Fei; Xu, Jian; Yin, Liangang; Wu, Di; Cui, Yunfei; Yan, Zheping; Chen, Tao

doi:10.1016/j.oceaneng.2022.112424

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…Deep learning and reinforcement learning have been applied in the motion control of autonomous underwater vehicles (AUVs) under conditions of actuator failure and unknown disturbances [30], [31]. To adapt to rapidly changing environments, a neural network has been used to accurately predict pitch angles [32], while a model-free RL-based controller has demonstrated potential in addressing timevarying dynamics [33].…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Propulsion Control for Amphibious Quadruped Robots With Dynamic Adaptation to Changing Environment

Yao,

Meng,

Zhang

et al. 2023

IEEE Robot. Autom. Lett.

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This paper proposes a learning-based adaptive propulsion control (APC) method for a quadruped robot integrated with thrusters in amphibious environments, allowing it to move efficiently in water while maintaining its ground locomotion capabilities. We designed the specific reinforcement learning method to train the neural network to perform the vector propulsion control. Our approach coordinates the legs and propeller, enabling the robot to achieve speed and trajectory tracking tasks in the presence of actuator failures and unknown disturbances. Our simulated validations of the robot in water demonstrate the effectiveness of the trained neural network to predict the disturbances and actuator failures based on historical information, showing that the framework is adaptable to changing environments and is suitable for use in dynamically changing situations. Our proposed approach is suited to the hardware augmentation of quadruped robots to create avenues in the field of amphibious robotics and expand the use of quadruped robots in various applications.

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Section: Related Workmentioning

confidence: 99%

Learning-Based Propulsion Control for Amphibious Quadruped Robots With Dynamic Adaptation to Changing Environment

Yao,

Meng,

Zhang

et al. 2023

IEEE Robot. Autom. Lett.

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“…Decision making for AMR has become a well-studied problem over the years [6], but safe decision making under uncertainties remains an open challenge. Many recent approaches use learning enabled components, such as deep neural networks (DNN) [4] and deep reinforcement learning (DRL) [5], to make quick decisions with a reasonable level of accuracy for many applications [1]. However, a vast majority of these techniques do not consider uncertainties and return only one decision, which might be incorrect in the presence of measurement or process noise at runtime [7].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, these different failures can often look the same to a human observer and can even cause confusion in state estimation-based [2] or bias measurement [3] approaches that have been proposed to deal with failures. Learning-based approaches that deal with such problems [4], [5], can encode more complex interactions in measurements, and can make better decisions, but even in such critical applications, these only return one decision and do not account for uncertainties. Thus, here we claim that if the robot could assess uncertainties by evaluating other Rahul Peddi and Nicola Bezzo are with the Departments of Systems and Information Engineering and Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

Peddi

Bezzo

2022

IEEE Robot. Autom. Lett.

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Autonomous mobile robots (AMR) operating in the real world often need to make critical decisions that directly impact their own safety and the safety of their surroundings. Learning-based approaches for decision making have gained popularity in recent years, since decisions can be made very quickly and with reasonable levels of accuracy for many applications. These approaches, however, typically return only one decision, and if the learner is poorly trained or observations are noisy, the decision may be incorrect. This problem is further exacerbated when the robot is making decisions about its own failures, such as faulty actuators or sensors and external disturbances, when a wrong decision can immediately cause damage to the robot. In this paper, we consider this very case study: a robot dealing with such failures must quickly assess uncertainties and make safe decisions. We propose an uncertainty aware learning-based failure detection and recovery approach, in which we leverage Decision Tree theory along with Model Predictive Control to detect and explain which failure is compromising the system, assess uncertainties associated with the failure, and lastly, find and validate corrective controls to recover the system. Our approach is validated with simulations and real experiments on a faulty unmanned ground vehicle (UGV) navigation case study, demonstrating recovery to safety under uncertainties.

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“…For the fast convergence of adaption in working environmental disturbances, Wadi et al devised a conditional adaptation law to tune the gains of kinematic and dynamic controllers in two ways: adaptive proportional control and universal adaptive stabilization-based control [21]. Moreover, intelligent methods, including fuzzy control [22], model predictive control [23], and reinforcement learning-based control [24], are combined with other control methods to improve the performance of trajectory tracking, taking into account complicated models, environments and system constraints.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

Wang,

et al. 2023

JMSE

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This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

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A general motion control architecture for an autonomous underwater vehicle with actuator faults and unknown disturbances through deep reinforcement learning

Cited by 14 publications

References 23 publications

Learning-Based Propulsion Control for Amphibious Quadruped Robots With Dynamic Adaptation to Changing Environment

Learning-Based Propulsion Control for Amphibious Quadruped Robots With Dynamic Adaptation to Changing Environment

An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

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