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IEEE International Conference on Networking, Sensing and control

作者： Boyu Li Weimin Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

With the increasing prevalence of automated guided vehicles (AGV s) in industry, the AG V dispatching has emerged as a research hotspot. Recently, matrix manufacturing systems (MMS) have been favored for their suitability in mass-customized production. However, fluctuating and diverse transport demands in MMS pose challenges for effective AGV scheduling. This paper investigates a multi-AGV dispatching problem (AGVDP) in MMS, involving time-constrained pick-up and delivery tasks. A mixed-integer linear programming model is developed to minimize total transportation costs, including travel distance, early arrival time, and utilized AGV s. To achieve this, an effective adaptive large neighborhood search algorithm (ALNS) is proposed. In ALNS, a scope-based nearest neighbor heuristic is introduced for initial construction. Diverse problem-specific operators are designed to generate large neighborhoods. To enhance ALNS's robustness to instance variations, an adaptive simulated annealing acceptance criterion is proposed. Computational experiments using a well-established benchmark validate the superiority of the proposed ALNS.

关键词： Adaptation models Sensitivity Transportation Simulated annealing Search problems Dispatching Sensors Time factors Tuning Manufacturing systems

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Whole-body Dynamic Collision Avoidance with Time-varying control Barrier Functions

Whole-body Dynamic Collision Avoidance with Time-varying Con...

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Chinese control and Decision Conference, CCDC

作者： Jihao Huang Xuemin Chi Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RCESDF is constructed in the robot body frame and solely relies on the robot’s shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach.

关键词： Silver Navigation Shape Numerical simulation Real-time systems Trajectory Collision avoidance

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Research on Optimization Seeking Method of Transmission Efficiency of Orbital Inspection Robot Based on DWPT System

Research on Optimization Seeking Method of Transmission Effi...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Youcheng Chen Zhitao Liu Xuchi Xue Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

In wireless power transmission system, due to the changes of coupling coefficient, load impedance and other factors, it destroys the system constant voltage, constant power output and transmission efficiency optimal working condition. Therefore, this paper proposes an optimization seeking method for the maximum transmission efficiency of the DWPT system of an orbital inspection robot with constant voltage and constant power output. Firstly, the LCC-S topology mathematical model with additional PI control circuit and the convex optimization model of transmission efficiency are established, and the gradient descent algorithm is used to iteratively search for the maximum value of transmission efficiency when the orbital coupling coefficient changes. Then, based on the mutual inductance model between coils, the coupling coefficients are converted into coil inductance parameters L 1 and L 2 , and the coil self-inductance is utilized for matching regulation to keep the transmission efficiency at the optimal value. Finally, in order to further optimize and predict the transmission efficiency, the fitting effects of the four fitting curves of the transmission efficiency are observed by the coefficient of determination and the residual paradigm, and the optimal fitting curves are used to calculate the maximum and average transmission efficiency, which reduces the system transmission loss and improves the charging efficiency.

关键词： Coils Couplings Inductance Fitting Inspection Orbits Mathematical models Integrated circuit modeling Optimization Robots

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Obstacle Based Fast Marching Tree for Global Motion Planning 48

Obstacle Based Fast Marching Tree for Global Motion Planning

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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Hou, Jiale Liu, Zhitao Su, Hongye Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper we introduce a novel method to improve the general performance of fast marching tree for collision-free path planning called obstacle-based fast marching tree. Our proposed OB-FMT∗ samples the map in discussion initially to obtain the vertexes of all obstacles and connect those vertexes to build a collision-free roadmap, an A*algorithm is adopted to find an optimal path in the built roadmap. Then multiple prolate hyper spheroids with focuses on vertexes of optimal path are established to limit the sampling range. Within the limited sampling point set, we utilize FMT∗ to find optimal solution with high efficiency. Theoretical analyses are given to prove the probabilistic completeness and computational complexity of our proposed method. Experiments in multiple scenarios show that our algorithm has a better performance comparing with other existing algorithms including RRT*, FMT*, Informed RRT*. © 2022 IEEE.

关键词： Motion planning

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SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

arXiv

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arXiv 2024年

作者： Liu, Yifan Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

Model Predictive control (MPC)-based trajectory planning has been widely used in robotics, and incorporating control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this paper, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

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Research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET cyber-systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in 3D Environments

arXiv

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arXiv 2024年

作者： Li, Songbo Luo, Shixin Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Time-delay Approach for Stabilization of Linear systems with Unknown control Direction

A Time-delay Approach for Stabilization of Linear Systems wi...

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IEEE Conference on Decision and control

作者： Gaofeng Pan Yang Zhu Zhengguang Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China and Ningbo Innovation Center Zhejiang University Ningbo China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a novel stability analysis for linear systems with unknown control directions, focusing on stabilization through extremum seeking to minimize a Lyapunov function. Our approach draws inspiration from Lie bracket systems, employing a transformation using time-delay analysis to convert the closed-loop system into a nominal time-delay system. Leveraging techniques from Input-to-state Stability (ISS), we provide a proof of semiglobal practical asymptotic stability. Compared to the preceding analysis based on Lie bracket systems, the use of time-delay methods transforms intricate mathematical discussions and proofs into a structured computational framework, facilitating a more intuitive understanding. Also, we propose a method for selecting controller parameters and can provide a more comprehensive explanation for the current high-gain parameter choices. Finally, we present a new control parameter to reduce the requirements of the frequency of the sinusoidal signals.

关键词： Linear systems Asymptotic stability Focusing Transforms Stability analysis Robustness Closed loop systems Frequency control Lyapunov methods

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Toward Understanding key Estimation in Learning Robust Humanoid Locomotion

arXiv

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arXiv 2024年

作者： Wang, Zhicheng Wei, Wandi Yu, Ruiqi Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations. © 2024, CC BY.

关键词： Anthropomorphic robots

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