Robust Co-Design: Coupling Morphology and Feedback Design Through Stochastic Programming

2021 ◽  
Author(s):  
Gabriel Bravo-Palacios ◽  
Gianluigi Grandesso ◽  
Andrea Del Prete ◽  
Patrick M. Wensing
Keyword(s):  
Stochastic Programming ◽  
Trajectory Optimization ◽  
State Of The Art ◽  
Tracking Error ◽  
Computational Design ◽  
Robotic Systems ◽  
Tracking Accuracy ◽  
Improved Performance ◽  
Uncertainty Models ◽  
New Framework

Abstract This paper proposes a new framework for the computational design of robots that are robust to disturbances. The framework combines trajectory optimization (TO) and feedback control design to produce robots with improved performance under perturbations by co-optimizing a nominal trajectory alongside a feedback policy and the system morphology. Stochastic-programming (SP) methods are used to address these perturbations via uncertainty models in the problem specification, resulting in motions that are easier to stabilize via feedback. Two robotic systems serve to demonstrate the potential of the method: a planar manipulator and a jumping monopod robot. The co-optimized robots achieve higher performance compared to state-of-the-art solutions where the feedback controller is designed separately from the physical system. Specifically, the co-designed controllers show higher tracking accuracy and improved energy efficiency (e.g., 91% decrease in tracking error and approximately 5% decrease in energy consumption for a manipulator) compared to LQR applied to a design optimized for nominal conditions.

Adaptive Proxy-Based Controller of an Active Ankle Foot Orthosis to Assist Lower Limb Movements of Paretic Patients

Robotica ◽  
2019 ◽  
Vol 37 (12) ◽  
pp. 2147-2164 ◽  
Author(s):  
Weiguang Huo ◽  
Victor Arnez-Paniagua ◽  
Guangzheng Ding ◽  
Yacine Amirat ◽  
Samer Mohammed
Keyword(s):  
Sliding Mode ◽  
State Of The Art ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Tracking Accuracy ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Ankle Foot Orthosis ◽  
Gait Phase ◽  
Foot Orthosis

SummaryThis paper deals with the control of an active ankle foot orthosis (AAFO) for paretic patients. State of the art methods using an AAFO try to track a predefined trajectory of the ankle joint while guaranteeing the wearer’s safety in the presence of a large tracking error. Combining the wearer’s safety and tracking accuracy is generally difficult to achieve at the same time, hence a trade-off should be found. Proxy-based sliding mode control (PSMC) offers great performances in both position tracking and safety guarantee. However, its tracking performance is subject to the influences of parameter uncertainties and external disturbances that generally occur during walking. This paper introduces an adaptation interaction method to the basic PSMC with an online adaptation of the proportional, integral and derivative parameters. At the same time, a gait phase-based ankle reference generation algorithm was proposed to adjust the joint reference trajectory in real time. The experiments using the AAFO show better tracking results with respect to basic PSMC while guaranteeing the safety.

scholarly journals A Survey of Robots in Healthcare

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Maria Kyrarini ◽  
Fotios Lygerakis ◽  
Akilesh Rajavenkatanarayanan ◽  
Christos Sevastopoulos ◽  
Harish Ram Nambiappan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The Elderly ◽  
Robotic Systems ◽  
Care Hospital ◽  
Robotic Technology ◽  
Persons With Disabilities ◽  
Survey Paper ◽  
Recent Advances ◽  
Art Research

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

Integrated optimal design of a high-speed planar parallel manipulator

2018 ◽  
Vol 233 (9) ◽  
pp. 2976-2990 ◽  
Author(s):  
Zhengsheng Chen ◽  
Minxiu Kong
Keyword(s):  
Optimal Design ◽  
Parallel Manipulator ◽  
High Speed ◽  
Design Method ◽  
Tracking Error ◽  
Dimensional Synthesis ◽  
Tracking Accuracy ◽  
Nsga Ii ◽  
Parameters Tuning ◽  
Planar Parallel Manipulator

To obtain excellent comprehensive performances of the planar parallel manipulator for the high-speed application, an integrated optimal design method, which integrated dimensional synthesis, motors/reducers selection, and control parameters tuning, is proposed, and the 3RRR parallel manipulator was taken as the example. The kinematic and dynamic performances of condition number, velocity index, acceleration capability, and low-order frequency are taken into accounts for the dimensional synthesis. Then, to match motors/reducers parameters and keep an economical cost, the constraint equations and the parameters library are built, and the cost is chosen as one of the optimization objectives. Also, to get high tracking accuracy, the dynamic forward plus proportional–derivative control scheme is introduced, and the tracking error is chosen as one of the optimization objectives. Hence, the optimization model including dimensional synthesis, motors/reducers selection and controller parameters tuning is established, which is solved by the genetic algorithm II (NSGA-II). The result shows that comprehensive performances can be effectively promoted through the proposed integrated optimal design, and the prototype was constructed according to the Pareto-optimal front.

scholarly journals Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoyi Long ◽  
Zheng He ◽  
Zhongyuan Wang
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Adaptive Dynamic Programming ◽  
Robotic Systems ◽  
Control Synthesis ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method.

scholarly journals Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yi Cui ◽  
Xintong Fang ◽  
Gaoqi Liu ◽  
Bin Li
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Tracking Error ◽  
Pseudospectral Method ◽  
Planning Problem ◽  
A Algorithm ◽  
Planning Scheme ◽  
Path Search ◽  
Simulation Results

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>

scholarly journals AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions

2018 ◽  
Author(s):  
John-William Sidhom ◽  
Drew Pardoll ◽  
Alexander Baras
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
State Of The Art ◽  
Class Ii ◽  
Class I ◽  
Binding Prediction ◽  
Translational Invariance ◽  
Mhc Molecules ◽  
Improved Performance

AbstractMotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.ResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.Availability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/[email protected]

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

scholarly journals The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775196 ◽  
Author(s):  
Ping Wang ◽  
Yabo Wang ◽  
He Huang ◽  
Feng Ru ◽  
Quan Pan
Keyword(s):  
Control Algorithm ◽  
Motion Tracking ◽  
Simulation Experiment ◽  
Tracking Error ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Rehabilitation Robots ◽  
Machine Interface ◽  
Repetitive Activities ◽  
Active Assistance

In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human–machine interface of other rehabilitation robots in future.

Autonomous Docking of Hybrid-Wheeled Modular Robots with an Integrated Active Genderless Docking Mechanism

2021 ◽  
pp. 1-36
Author(s):  
Shubhdildeep S. Sohal ◽  
Bijo Sebastian ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Visual Servoing ◽  
Mechanical Design ◽  
Low Cost ◽  
Image Plane ◽  
Robotic Systems ◽  
Modular Robots ◽  
Tracking Accuracy ◽  
Drive Mechanism ◽  
Docking Mechanism ◽  
Autonomous Docking

Abstract This paper presents a self-reconfigurable modular robot with an integrated 2-DOF active docking mechanism. Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of large systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. The proposed self-reconfigurable mobile robot design exhibits dual mobility using a tracked drive mechanism for longitudinal locomotion and a wheeled drive mechanism for lateral locomotion. The 2-DOF docking interface allows for efficient docking while tolerating misalignments. To aid autonomous docking, visual marker-based tracking is used to detect and re-position the source robot relative to the target robot. The tracked features are then used in Image-Based Visual Servoing to bring the robots close enough for the docking procedure. The hybrid-tracking algorithm allows eliminating external pixelated noise in the image plane resulting in higher tracking accuracy along with faster frame update on a low-cost onboard computational device. This paper presents the overall mechanical design and the integration details of the modular robotic module with the docking mechanism. An overview of the autonomous tracking and docking algorithm is presented along-with a proof-of-concept real world demonstration of the autonomous docking and self-reconfigurability. Experimental results to validate the robustness of the proposed tracking method, as well as the reliability of the autonomous docking procedure, are also presented.

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