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arXiv 2024年

作者： Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Department of Electrical and Computer Engineering NYU Tandon School of Engineering 5 Metrotech Center BrooklynNY11201 United States

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer’s estimation accuracy and the fulfillment of safety and control requirements. © 2024, CC BY.

关键词： Lyapunov functions

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Proactive Robot Control for Collaborative Manipulation Using Human Intent

arXiv

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arXiv 2023年

作者： Rysbek, Zhanibek Li, Siyu Shervedani, Afagh Mehri Žefran, Miloš The Robotics Laboratory Department of Electrical and Computer Engineering The University of Illinois Chicago ChicagoIL60607 United States

Collaborative manipulation task often requires negotiation using explicit or implicit communication. An important example is determining where to move when the goal destination is not uniquely specified, and who should lead the motion. This work is motivated by the ability of humans to communicate the desired destination of motion through back- and-forth force exchanges. Inherent to these exchanges is also the ability to dynamically assign a role to each participant, either taking the initiative or deferring to the partner's lead. In this paper, we propose a hierarchical robot control framework that emulates human behavior in communicating a motion destination to a human collaborator and in responding to their actions. At the top level, the controller consists of a set of finite-state machines corresponding to different levels of commitment of the robot to its desired goal configuration. The control architecture is loosely based on the human strategy observed in the human-human experiments, and the key component is a real-time intent recognizer that helps the robot respond to human actions. We describe the details of the control framework, and feature engineering and training process of the intent recognition. The proposed controller was implemented on a UR10e robot (Universal Robots) and evaluated through human studies. The experiments show that the robot correctly recognizes and responds to human input, communicates its intent clearly, and resolves conflict. We report success rates and draw comparisons with human-human experiments to demonstrate the effectiveness of the approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization

Gait Optimization for Legged Systems Through Mixed Distribut...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Tsikelis Konstantinos Chatzilygeroudis Computer Engineering and Informatics Department University of Patras Patras Greece Department of Electrical & Computer Engineering Laboratory of Automation and Robotics (LAR) University of Patras Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling.

关键词： Pipelines Closed box Humanoid robots Graphics processing units Real-time systems Planning Quadrupedal robots Optimization Trajectory optimization Convergence

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Data-Efficient Control Barrier Function Refinement

Data-Efficient Control Barrier Function Refinement

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American Control Conference (ACC)

作者： Bolun Dai Heming Huang Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY

Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a two-link arm.

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Whole Body Balance Control for Bipedal Robots Based on Virtual Model Control 16th

Whole Body Balance Control for Bipedal Robots Based on Virtu...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Gao, Chengzhi Xie, Ye Zhu, Yiting Liang, Dingkun Kong, Lingyu Wang, Xin Xie, Anhuan Gu, Jianjun Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964918

Due to the lack of accurate stability theoretical basis, the dynamic balance control of bipedal robots is a challenging topic. In this paper, we propose a controller that combines virtual model control and whole body control. In our framework, the whole system is simplified into a spring inverted pendulum model. Decoupling control is carried out for this model, and a virtual model is introduced to establish the relationship between the joint torque of the support leg and the state of the center of mass. The distribution of reaction force is obtained through decoupling closed-loop control. The whole body control part first uses multi task whole body control to determine the kinematics information, and then solves the joint feedforward torque through dynamical model, thus obtaining the desired force position hybrid control. Proposed algorithm improved the problem of incomplete decoupling between states and increased the stiffness of the support leg. The newly designed control framework was tested in Simscape dynamic simulation and verified the anti-interference ability and fast walking ability of Whole Body Balance Control for bipedal robots based on Virtual Model Control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Inverted pendulum

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arXiv

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arXiv 2024年

作者： Lin, Zhenchao He, Li Yang, Hongqiang Sun, Xiaoqun Zhang, Guojin Chen, Weinan Guan, Yisheng Zhang, Hong School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China Meituan Technology Co. Ltd Shenzhen China

Large-scale point cloud consists of a multitude of individual objects, thereby encompassing rich structural and underlying semantic contextual information, resulting in a challenging problem in efficiently segmenting a point cloud. Most existing researches mainly focus on capturing intricate local features without giving due consideration to global ones, thus failing to leverage semantic context. In this paper, we propose a Similarity-Weighted Convolution and local-global Fusion Network, named SWCF-Net, which takes into account both local and global features. We propose a Similarity-Weighted Convolution (SWConv) to effectively extract local features, where similarity weights are incorporated into the convolution operation to enhance the generalization capabilities. Then, we employ a downsampling operation on the K and V channels within the attention module, thereby reducing the quadratic complexity to linear, enabling Transformer to deal with large-scale point cloud. At last, orthogonal components are extracted in the global features and then aggregated with local features, thereby eliminating redundant information between local and global features and consequently promoting efficiency. We evaluate SWCF-Net on large-scale outdoor datasets SemanticKITTI and Toronto3D. Our experimental results demonstrate the effectiveness of the proposed network. Our method achieves a competitive result with less computational cost, and is able to handle large-scale point clouds efficiently. The code is available at https://***/Sylva-Lin/SWCF-Net Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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A Novel Stiffness Programmable Method for Environment-adaptive Crawling Robot

A Novel Stiffness Programmable Method for Environment-adapti...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Luo, Yudong Zhao, Na Shen, Yantao Dalian Maritime University Morphing Robotics Laboratory Department of Computer Science and Technology Liaoning Dalian116021 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

Benefitting from the adjustable body stiffness, crawling animals such as earthworms can crawl in soil with different hardnesses. To address one of the main challenges of variable stiffness in crawling robots, this paper presents a new concept of tuning stiffness characteristics based on the mechanical stiffening method, constructs the stiffness model of the robot, and performs a proof-of-concept based on a custom-built crawling robot prototype. The robot is composed of a group of specially designed scissor elements, which allows it to achieve bidirectional continuous deformation, thereby enabling continuous stiffness regulation. The stiffness can be adjusted simply by programming the input to the actuators or modi-fying the number of scissor elements, making it easy control, fast response, and high energy efficiency. Then, the stiffness mathematical model of the novel crawling robot segment is established, and the physical parameters are excavated on the basis of numerous numerical simulations. Finally, the 3D-printed prototype of the robot is constructed for experiments, and the results demonstrate the feasibility of tuning the stiffness characteristics based on the proposed scissor element enabled mechanism, which provides a new perspective on the way to change the stiffness of the crawling robot. © 2022 IEEE.

关键词： Stiffness

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Fast Walking of Position-Controlled Biped Robot Based on Whole-Body Compliance Control 16th

Fast Walking of Position-Controlled Biped Robot Based on Who...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhu, Yiting Xie, Ye Gao, Chengzhi Liang, Dingkun Kong, Lingyu Wang, Xin Xie, Anhuan Gu, Jianjun Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964918

When a biped robot walks at a high speed, only relying on the open-loop gait trajectory based on the linear inverted pendulum model (LIPM), the robot cannot maintain stability. Sudden contact with the ground when landing may cause the robot to be unstable. Simultaneously, when the robot walks faster, the horizontal position of the center of mass (CoM) varies greatly during a walk cycle, and the expected CoM trajectory cannot be accurately tracked, resulting in a deviation from the zero-moment point (ZMP). Aiming at the above situation that may occur in fast walking, a whole-body compliance controller with emphasis on making robot’s walking more stable and faster is presented in this paper. The whole-body compliance controller consists of three parts: the CoM compliance controller, the vertical compliance controller and the foot compliance controller, which are used to improve the tracking effect of CoM, reduce the force impact of foot, and adjust the posture of the foot respectively. The simulation results show that with the whole-body compliance controller proposed in this paper, the tracking accuracy of the CoM becomes better, the posture of the robot is more stable during walking, and the landing compliance during landing are greatly improved. Finally, the walking speed of the robot can reach 1.8m/s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Compliance control

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Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

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Biomedical engineering Frontiers 2022年第1期3卷 1-13页

作者： Angela Zhang Amil Khan Saisidharth Majeti Judy Pham Christopher Nguyen Peter Tran Vikram Iyer Ashutosh Shelat Jefferson Chen B.S.Manjunath Vision Research Laboratory Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraSanta BarbaraCAUSA Chen Lab Department of NeurosurgeryUniversity of CaliforniaIrvine Medical CenterOrangeCAUSA Santa Barbara Cottage Hospital Santa BarbaraCAUSA

Objective and Impact *** propose an automated method of predicting Normal Pressure Hydrocephalus(NPH)from CT scans.A deep convolutional network segments regions of interest from the *** regions are then combined with MRI information to predict *** our knowledge,this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for *** to their low cost and high versatility,CT scans are often used in NPH *** well-defined and effective protocol currently exists for analysis of CT scans for ***’index,an approximation of the ventricle to brain volume using one 2D image slice,has been proposed but is not *** proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting *** propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these *** segmentation and network features are used to train a model for NPH *** method outperforms the current state-of-the-art by 9 precision points and 29 recall *** segmentation model outperforms the current state-of-the-art in segmenting the ventricle,gray-white matter,and subarachnoid space in CT *** experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process,and network properties can increase NPH prediction accuracy.

关键词： diagnosis network Connectivity

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