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

作者： Niu, Haoyi Wu, Weimin Zhang, Tao Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

Charging problem for the AGVs (automated guided vehicles) in a multi-robot system based on a real-world intelligent warehouse aims to maximize the transportation efficiency of AGVs considering the charging process. For this purpose, a hierarchical charging algorithm is presented by taking into account both the continuity of transportation and the charging efficiency. A multi-level threshold scheme is designed to reduce the impact of the charging process on AGV scheduling. The numerical experiment results indicate that at the same charging rate, the number of completed tasks by AGVs can be increased from 18 to 28 an hour compared with traditional charging algorithm thus the efficiency can be improved from 34.62% to 51.86% correspondingly, which shows the superior performance of the presented algorithm. © 2021 IEEE.

关键词： Multipurpose robots

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A Hybrid Algorithm to Solve Periodic Vehicle Dispatching Problem with Heterogeneous Vehicles in Logistics Transportation 18

A Hybrid Algorithm to Solve Periodic Vehicle Dispatching Pro...

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

作者： Wang, Xingkai Xing, Zichao Wu, Weimin Chen, Xinyu Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

The vehicle dispatching problem (VDP) is researching how to arrange vehicles' transportation, which has been addressed by many scholars. In the field of logistics transportation, the vehicle dispatching problem is presented in a special form, that is, the periodic vehicle dispatching problem (PVDP) with heterogeneous vehicles. In order to solve this problem and improve the efficiency of vehicles, a round recombination algorithm (RRA) with two operators are designed in this paper. And a hybrid algorithm consisting of RRA and simulated annealing algorithm is also proposed to improve RRA. Three groups of experiments are carried out to test the performance of designed algorithms in different scenarios. The results of these experiments prove the effectiveness of RRA. The results also show the superiority of hybrid algorithm to RRA. © 2021 IEEE.

关键词： Simulated annealing

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A Collision Avoidance Algorithm for Idle Robots in Multi-robot System 18

A Collision Avoidance Algorithm for Idle Robots in Multi-rob...

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

作者： Xing, Zichao Wu, Weimin Wang, Xingkai Hu, Ruifen Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

Multi-robot systems (MRSs) play vital roles in manufacturing systems and logistics systems. In order to improve the efficiency of MRSs, we propose an idle robot collision avoidance algorithm(IRCAA) for idle robots to give way for working robots. In the proposed method, idle robots stop in place for new tasks without returning to robot stations. Once an idle robot gets in the way of working robots, IRCAA will find an avoidance point (AP) for the idle robot to give way. Additionally, no new block or deadlock occurs when the idle robot arrives at the AP. The proposed method in this paper is effective and able to improve the efficiency of MRSs, which is verified by both simulation experiments and actual industrial projects. © 2021 IEEE.

关键词： Multipurpose robots

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Research on Coupled Task Allocation and Scheduling of Multi-type Robots 18

Research on Coupled Task Allocation and Scheduling of Multi-...

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

作者： Wang, Xingkai Xing, Zichao Wu, Weimin Chen, Xinyu Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

Task allocation problem and scheduling problem have always been hot topics in the robotics research field. Both of them can be divided into many categories. Among all these categories, coupled task allocation and scheduling problem (CTASP) is a quite difficult problem, especially for of multi-type robots. However, few researches are conducted on this area. Usually, the CTASP is studied for multi-type robots with fixed execution sequence in the reported literature. However, this paper will study the CTASP for multi-type robots with flexible execution sequence. A two-dimension genetic algorithm (TDGA) is designed to solve this type of problem, combined with rank minimal heuristic (RMH) algorithm to provide an elite strategy. The simulation experiment proves that TDGA can solve this kind of problems efficiently, and the efficiency of hybrid genetic algorithm has been further improved. © 2021 IEEE.

关键词： Robots

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Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture

arXiv

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arXiv 2025年

作者： Lu, Wangtao Wei, Yufei Xu, Jiadong Jia, Wenhao Li, Liang Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Information and Engineering Zhejiang University of Technology Hangzhou310023 China

Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improved Hybrid Gaussian Process Regression Model for state-of-Health Estimation of Li-ion Batteries

Improved Hybrid Gaussian Process Regression Model for State-...

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Chinese Automation Congress (CAC)

作者： Tao An Zhitao Liu Chizhou Yan Ke Xu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate state-of-health (SOH) estimation con-tributes to the healthy and stable operation of the Li-ion batteries. This paper proposes an improved hybrid Gaussian process regression (GPR) method incorporating physical information for the high-accuracy estimation of battery SOH. The estimation process considers the effects of temperature, discharge rate, and depth of discharge (DOD) on the battery health, and the relevant features of the electrode degradation process are extracted for battery SOH estimation. The kernel function of GPR comprehensively considers the mechanical characteristics of battery degradation and can effectively track the battery degra-dation process. The validation experiments were conducted on the A123 LFP/Graphite battery dataset. Sufficient comparative experiments have validated the accuracy and robustness of the proposed method.

关键词： Lithium-ion batteries Electrodes Degradation Accuracy Estimation Gaussian processes Feature extraction Robustness Batteries Kernel

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Battery Charge Dispatching in Multi-robot systems 18

Battery Charge Dispatching in Multi-robot Systems

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

作者： Xing, Zichao Wu, Weimin Niu, Haoyi Hu, Ruifen Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

Multi-robot systems (MRSs) attract more and more attention due to their efficiency and safety. In a practical scenario, robots need to work for a long time without human intervention, which requires the robots do not run out of power. The battery charging strategy of most systems uses a fixed threshold or fixed periods to determine when robots should be charged. It does not consider the system task situation, nor the situation of charging station (CS) competition. This paper proposes an efficient charge dispatching strategy, which can ensure that a robot firstly completes a task quickly when it is scheduled to work and firstly be charged to be ready for a subsequent task when it is idle. For the case that multiple robots compete for the same CS, we designe a bidding strategy to determine which robot should be serviced. A rule-based charging strategy is used to compare with the proposed charge dispatching strategy in this paper. The results show that our method performs better in terms of stability and efficiency. © 2021 IEEE.

关键词： Multipurpose robots

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Bayesian CNN-GRU with Attention Mechanism for Lithium-ion Battery state of Health Estimation

Bayesian CNN-GRU with Attention Mechanism for Lithium-ion Ba...

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Chinese control and Decision Conference, CCDC

作者： Tao An Zhitao Liu Chizhou Yan Ke Xu Shuxing Zhang Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurate estimation of the state of health (SOH) is crucial for ensuring the safe and stable operation of lithium-ion (Li-ion) batteries. This paper presents an approach called Bayesian CNN-GRU with Attention Mechanism (AM-BCG) to estimate the entire lifecycle health state of the battery. Effective feature extraction is performed during the SOH estimation to ensure accurate prediction. Specifically, domain knowledge- related temporal features of Li-ion battery degradation processes are extracted, while capacity difference curves are utilized as spatially relevant features for model inputs. The AM-BCG approach comprises parallel CNN-GRU networks with attention mechanism and bayesian neural networks (BNN). The parallel CNN-GRU networks extract generalized information from prior data, the attention mechanism increases the weight of essential features and reduces overfitting, and BNN provides uncertainty estimates. Experimental validation on the A123 67Ah LFP battery dataset confirms that the proposed method exhibits high robustness and accuracy in estimating battery SOH.

关键词： Lithium-ion batteries Attention mechanisms Accuracy Uncertainty Neural networks Estimation Feature extraction

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Global-state-Free Obstacle Avoidance for Quadrotor control in Air-Ground Cooperation

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IEEE Robotics and Automation Letters 2025年第7期10卷 6688-6695页

作者： Zhang, Baozhe Chen, Xinwei Chen, Qingcheng Xu, Chao Gao, Fei Cao, Yanjun Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China Zhejiang University Huzhou Institute Huzhou313000 China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai200062 China

CoNi-MPC [Zhang et al. (2023)] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooperative Non-inertial frame-based Obstacle Avoidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: 1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states;2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction;and 3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios. © 2016 IEEE.

关键词： Quadrotors Collision avoidance Dynamics Autonomous aerial vehicles Trajectory Laser radar Air to ground communication Real-time systems Modulation Dynamical systems

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Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space

arXiv

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arXiv 2024年

作者： Wang, Qianhao Wang, Zhepei Wang, Mingyang Ji, Jialin Han, Zhichao Wu, Tianyue Jin, Rui Gao, Yuman Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China

Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods struggle with balancing high-quality output and efficiency. Moreover, another crucial requirement for convex polytopes to accurately contain certain seed point sets, such as a robot or a front-end path, is proposed in various tasks, which we refer to as manageability. In this paper, we propose Fast Iterative Regional Inflation (FIRI) to generate high-quality convex polytope while ensuring efficiency and manageability simultaneously. FIRI consists of two iteratively executed submodules: Restrictive Inflation (RsI) and Maximum Volume Inscribed Ellipsoid (MVIE) computation. By explicitly incorporating constraints that include the seed point set, RsI guarantees manageability. Meanwhile, iterative MVIE optimization ensures high-quality result through monotonic volume bound improvement. In terms of efficiency, we design methods tailored to the low-dimensional and multi-constrained nature of both modules, resulting in orders of magnitude improvement compared to generic solvers. Notably, in 2-D MVIE, we present the first linear-complexity analytical algorithm for maximum area inscribed ellipse, further enhancing the performance in 2-D cases. Extensive benchmarks conducted against state-of-the-art methods validate the superior performance of FIRI in terms of quality, manageability, and efficiency. Furthermore, various real-world applications showcase the generality and practicality of FIRI. The high-performance code of FIRI will be open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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