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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

arXiv

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arXiv 2024年

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen The Flight Dynamics & Control/Hybrid Systems Lab Purdue University West LafayetteIN47907 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Department of Computer Science and Engineering University at Buffalo NY14260 United States

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents). © 2024, CC BY.

关键词： Traveling salesman problem

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Trajectory Tracking Error Optimization based on Iterative Learning 4

Trajectory Tracking Error Optimization based on Iterative Le...

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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Huang, Haoran Zhang, Tao Jia, Danping Zhang, Hualiang Yan, Hongyu Pei, Quanshen Ding, Fan Shenyang University of Technology School of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The X-Y precision plane motion platform is widely used in various precision motion control fields. In this paper, to improve the tracking accuracy of the X-Y precision plane motion table, a high-level iterative learning control with forgetting factor is proposed, and it is combined with a kind of PD-type iterative learning control with low-pass filter, and the method is applied to the Simulink model of the linear motor of the X-Y precision plane platform, thereby improving the trajectory accuracy of the X-Y precision plane motion platform. The traditional ILC has the disadvantage that the more number of iterations, the more divergent tracking error is. The new ILC proposed in this paper can not only effectively reduce the noise, reduce the tracking error, but also eliminate the problem of error divergence caused by too many iterations. At last, the experimental results of high-order PD iterative learning with forgetting factor and low-pass filter, PD iterative learning with low-pass filter, ordinary PD iterative learning are compared. The results show that the high-order PD iterative learning with forgetting factor and low-pass filter has better tracking effect than the first-order iterative learning and other several algorithms. It can make the movement trajectory of the X-Y precision plane motion platform more stable and improve the accuracy. © 2022 IEEE.

关键词： Low pass filters

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Effects of Robotic Expertise and Task Knowledge on Physical Ergonomics and Joint Efficiency in a Human-Robot Collaboration Task

Effects of Robotic Expertise and Task Knowledge on Physical ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Matteo Pantano Arianna Curioni Daniel Regulin Tobias Kamps Dongheui Lee Technology department Siemens Aktiengesellschaft Munich Germany Department of Electrical and Computer Engineering Human-Centered Assistive Robotics Technical University of Munich Munich Germany Autonomous Systems Institut für Computertechnik Technische Uni-versitat Wien Vienna Austria Institute of Robotics and Mechatronics German Aerospace Center Wessling Germany

With the trend of low batch manufacturing, more and more small and medium enterprises are leaning towards adopting collaborative robots to increase productivity and improve operator well-being. However, robots are often programmed by robot experts rather than the operators effectively working with the machine. Therefore, operators may perceive low levels of task autonomy due to the unpredictability of robot motions. Empowering operators to make their own choices regarding robot motions can improve such feelings. However, research in cognitive science shows that allowing operators to decide on robot motions in a collaborative task could be influenced by how people consider their travel path and their partner's action. To better understand these relations, considering preliminary results from a previous study, we designed a user study where we tested operators' decisions in a collaborative task where groups of robot experts and novices were asked to choose their preferred task configuration among four possible options that differed in terms of operators' physical ergonomics and robot travel path. Our results show that robotic experts prioritize joint team work rather than their ergonomics. Contrarily, novices prioritize individual efforts and tend to reduce the robot travel path while keeping their travel path constant, maintaining good physical ergonomics. In conclusion, providing operators with task decision autonomy can be advantageous, but operator background must be considered to ensure optimal physical ergonomics and travel paths.

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Research on prefabricated component production line mold platform combination method

Research on prefabricated component production line mold pla...

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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Yu, Lingfeng Li, Bo Qi, Yuanwei School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

In traditional prefabricated component manufacturing enterprises, there are often problems such as low utilization rate of the platform surface due to poor production plans of the enterprise, resulting in waste of production resources and low production capacity of the enterprise. In order to solve the problem of mold platform combination allocation in the production process of prefabricated component manufacturers, a mold platform combination allocation method based on the combination of machine learning and backtracking is proposed. The backtracking method is used to search for the theoretical best fit combination result, and the improved BL is used. The positioning algorithm simulates the placement process of the mold on the platform, and uses the Apriori algorithm to train the obtained data set to obtain the association rules contained in the frequent item set. When the enterprise re-produces, the prefabricated components are combined according to the association rules for production, and the utilization rate of the platform is improved. The simulation test is carried out with the example data of the prefabricated component manufacturing enterprise, which verifies the effectiveness of the combination allocation method of mold and platform combined with the backtracking method and the Apriori algorithm to solve the problem. © Published under licence by IOP Publishing Ltd.

关键词： Production control

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Flow Sensing-Based Underwater Target Detection Using Distributed Mobile Sensors

Flow Sensing-Based Underwater Target Detection Using Distrib...

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IEEE Conference on Decision and Control

作者： Jun Wang Tongsheng Shen Dexin Zhao Feitian Zhang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China National Innovation Institute of Defense Technology Beijing China Department of Advanced Manufacturing and Robotics and the State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper presents a novel bioinspired underwater target detection method with distributed mobile sensors. Inspired by the fish's lateral line system, distributed mobile sensors are proposed to measure their respective local flow pressure and collectively estimate the parameters (e.g., position and dimension) of underwater targets with dynamic sensor trajectory planning. This paper formulates an optimal trajectory planning problem for a group of mobile sensors using information entropy as the cost function, with an aim to maximize the confidence level in the estimated target parameters. This paper in particular investigates the detection of a circular target in a uniform flow. A Bayesian filter is adopted to assimilate the distributed pressure measurements dynamically to estimate the position and radius of given circular targets. To verify the effectiveness of the proposed method, simulation is conducted with both fixed and moving circular targets in uniform flow. Simulation results are presented and analyzed.

关键词： Trajectory planning Simulation Object detection Robot sensing systems Sensor systems Entropy Sensors

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Nonlinear Model Predictive Control Approximation: Applications to Truck-Trailer Control System

Nonlinear Model Predictive Control Approximation: Applicatio...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Suyong Park Duc Giap Nguyen Yongsik Jin Jinrak Park Dohee Kim Jeong Soo Eo Kyoungseok Han Department of Automotive Engineering Hanyang University Seoul Republic of Korea Department of Mechanical Engineering Kyungpook National University Daegu Republic of Korea Robotics IT Convergence Research Section Electronics and Telecommunications Research Institute (ETRI) Daegu Republic of Korea Department of Electrified Systems Control Research Lab Hyundai Motor Company Hwaseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Training Tracking Simulation Training data Artificial neural networks Predictive models Control systems Real-time systems Safety Predictive control

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Research on optimization method of routing buffer linkage based on Q-learning

Research on optimization method of routing buffer linkage ba...

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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Qi, Hongfeng Chang, Daliang Gong, Wei Dai, Shilong School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Self-Organization and Collaboration in Robotic Manufacturing systems

Self-Organization and Collaboration in Robotic Manufacturing...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Andreas Frotzscher Xiao Chen Duy Lam Tran Thore Goll Hamid Sadeghian Fan Wu Martin Dehmel Tommy Wohlfahrt Matthias Riedl Sami Haddadin Fraunhofer Institute for Integrated Circuits IIS Engineering of Adaptive Systems EAS Dresden Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany ifak e.V. Magdeburg Germany Franka Emika GmbH Munich Germany

The flexibility and adaptability of smart production systems are achieved through the use of autonomous and collaborating production assets. However, these systems still heavily rely on a central control unit to supervise and coordinate assets, as well as adapt to new demands. Additionally, these systems require reliable wireless connectivity to function effectively. This paper summarizes three contributions to autonomous manufacturing systems. Firstly, a system architecture that co-designs communication control for collaborating production assets is proposed, enabling dynamic trade-off negotiations between the control application and the communication network. Secondly, a novel approach for the self-organization of assets that can adapt in real-time is introduced. Finally, we present a collaborating strategy that facilitates close collaboration between robots while considering the performance of the wireless communication system in use.

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Solving Colored Traveling Salesman Problem via Multi-neighborhood Simulated Annealing Search 18

Solving Colored Traveling Salesman Problem via Multi-neighbo...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Yangming Xu, Wenqiang Fu, Zhang-Hua Zhou, Mengchu East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen518172 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Department of Cyber-Physical Systems St. Petersburg State Marine Technical University St. Petersburg198262 Russia

ISBN: (纸本)9781665440486

A colored traveling salesman problem (CTSP) is an important variant of the well-known multiple traveling salesman problem, which uses colors to differentiate salesmen's accessibility to individual cities to be visited. As a highly useful model for some complex scheduling problems, CTSP is NP-hard. A Multi-neighborhood Simulated Annealing Search (MSAS) approach is proposed to solve it in this paper. Starting from an initial solution, it iterates through two complementary neighborhoods: intra-route and inter-route neighborhoods. Experiments on three groups of 60 widely-used benchmark instances show that it achieves highly competitive performance compared to state-of-the-art algorithms. Moreover, MSAS can be integrated into other search methods to further improve performance, which is demonstrated by using a recently proposed iterated two-phase local search. © 2021 IEEE.

关键词： Traveling salesman problem

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