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Dynamic-Aware and Static Context Network for large-scale 3D place recognition

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Knowledge-Based systems 2025年 319卷

作者： Liao, Ming Di, Xiaoguang Liu, Maozhen Lv, Teng Zhang, Xiaofei Zhu, Runwen Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

3D point cloud-based place recognition enables robots to obtain precise global positions without GPS, correct trajectory drift in SLAM, and recover from the kidnapped robot problem. However, in outdoor environments, the presence of moving objects can cause occlusions in point clouds and introduce noise into the data, leading to localization failures. To address this issue, we propose a Dynamic-Aware and Static Context Network (DASC-Net) for large-scale 3D place recognition. Our approach leverages the spatio-temporal consistency of point cloud sequences to accurately segment dynamic objects while incorporating static point cloud context to compensate for feature loss caused by noise interference or occlusions from dynamic objects, thereby enhancing robustness and generalization. Specifically, DASC-Net adopts a two-stage strategy: first, it introduces a coarse-to-fine moving object segmentation method to effectively eliminate dynamic noise;second, it utilizes spatial context association and multi-scale feature aggregation to improve static feature representation and matching. Extensive experimental results demonstrate that DASC-Net outperforms existing place recognition approaches, particularly in dynamic scenes. © 2025

关键词： Image segmentation

Primitive-Planner: An Ultra Lightweight Quadrotor Planner with Time-optimal Primitives

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arXiv 2025年

作者： Hou, Jialiang Pan, Neng Wang, Zhepei Ji, Jialin Guan, Yuxiang Gan, Zhongxue Gao, Fei Academy for Engineering and Technology Fudan University Shanghai200433 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

It is a significant requirement for a quadrotor trajectory planner to simultaneously guarantee trajectory quality and system lightweight. Many researchers focus on this problem, but there’s still a gap between their performance and our common wish. In this paper, we propose an ultra lightweight quadrotor planner with time-optimal primitives. Firstly, a novel motion primitive library is proposed to generate time-optimal and dynamical feasible trajectories offline. Secondly, we propose a fast collision checking method with a deterministic time consumption, independent of the sampling resolution of the primitives. Finally, we select the minimum cost trajectory to execute among the safe primitives based on user-defined requirements. The propsed transformation relation between the local trajectories ensures the smoothness of the global trajectory. The planner reduces unnecessary online computing power consumption as much as possible, while ensuring a high-quality trajectory. Benchmark comparisons show that our method can generate the shortest flight time and distance of trajectory with the lowest computation overload. Challenging real-world experiments validate the robustness of our method. Copyright © 2025, The Authors. All rights reserved.

关键词： Flight paths

Dynamic Data Collection for UAV-Assisted Green Industrial IoT

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IEEE Internet of Things Journal 2025年

作者： Lu, Jiarong Wang, Ying Zhao, Junwei Wu, Wen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China North Automatic Control Technology Institute China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China

Unmanned aerial vehicles (UAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of UAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, UAV speed variation brings challenges to achieving green communication and reliable data collection. In this paper, we propose a dynamic UAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of UAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize UAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and UAV speed are jointly optimized for minimizing the long-term energy consumption of UAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed UAV speed scheme. © 2014 IEEE.

关键词： Energy utilization

Robust Mean Field Social control: A Unified Reinforcement Learning Framework

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arXiv 2025年

作者： Xu, Zhenhui Chen, Jiayu Wang, Bing-Chang Wu, Yuhu Shen, Tielong Department of Systems and Control Engineering The Institute of Science Tokyo Tokyo152-8552 Japan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

This paper studies linear quadratic Gaussian robust mean field social control problems in the presence of multiplicative noise. We aim to compute asymptotic decentralized strategies without requiring full prior knowledge of agents’ dynamics. The primary challenges lie in solving an indefinite stochastic algebraic Riccati equation for feedback gains, and an indefinite algebraic Riccati equation for feedforward gains. To overcome these challenges, we first propose a unified dual-loop iterative framework that simultaneously handles both indefinite Riccati-type equations, and provide rigorous convergence proofs for both outer-loop and inner-loop iterations. Second, recognizing that biases may arise in iterative processes due to estimation and modeling errors, we analyze the robustness of the proposed algorithm by using the small-disturbance input-to-state stability technique. This ensures convergence to a neighborhood of the optimal solution, even in the existence of disturbances. Finally, to address the limitation of requiring precise knowledge of agents’ dynamics, we employ the integral reinforcement learning technique to develop a data-driven method within the dual-loop iterative framework. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词：

Convergence rates for an inexact linearized ADMM for nonsmooth nonconvex optimization with nonlinear equality constraints

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arXiv 2025年

作者： Bourkhissi, Lahcen El Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

In this paper, we consider nonconvex optimization problems with nonsmooth nonconvex objective function and nonlinear equality constraints. We assume that both the objective function and the functional constraints can be separated into 2 blocks. To solve this problem, we introduce a new inexact linearized alternating direction method of multipliers (ADMM) algorithm. Specifically, at each iteration, we linearize the smooth part of the objective function and the nonlinear part of the functional constraints within the augmented Lagrangian and add a dynamic quadratic regularization. We then compute the new iterate of the block associated with nonlinear constraints inexactly. This strategy yields subproblems that are easily solvable and their (inexact) solutions become the next iterates. Using Lyapunov arguments, we establish convergence guarantees for the iterates of our method toward an ϵ-first-order solution within O(ϵ−2) iterations. Moreover, we demonstrate that in cases where the problem data exhibit e.g., semi-algebraic properties or more general the KL condition, the entire sequence generated by our algorithm converges, and we provide convergence rates. To validate both the theory and the performance of our algorithm, we conduct numerical simulations for several nonlinear model predictive control and matrix factorization problems. © 2025, CC BY.

关键词： Matrix factorization

A Velocity Tracking Approach Based on Neural Networks for a Multimodal Wheel-Legged Composite Gait on a Parallel Structure Robot

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IEEE/ASME Transactions on Mechatronics 2025年

作者： Xue, Junfeng Wang, Shoukun Xu, Yongkang Wang, Junzheng Beijing Institute of Technology School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

Parallel-legged robots are favored by many researchers due to their potent environmental adaptability and load capacity. However, the work efficiency of the parallel leg structure is always confined due to its uptight workspace and extremely uneven force distributed among actuators. Therefore, this article proposes a new type of wheel-legged composite gait for the parallel structure, inspired by the characteristic of variation in the pitch angle of the foot-end when a human walks. First, the kinematic inverse solution of the wheel-legged composite gait is derived, where the reverse rotation of the wheel alleviates the slipping phenomenon of the foot-end on stance stage in humanoid gait from the kinematic level. Meanwhile, numerical simulation and experiments have verified that the proposed gait can enlarge the workspace of a single leg for 25.4% and alleviate the maximum force on the actuators for 16.4% minimizing actuator wear. Besides, a legged motion speed tracking approach is implemented for dynamics of the wheel-legged composite gait. Among them, a whole body dynamics model is constructed to adjust the neural network weights enabling real-time adjustment of incremental proportion–integration–differentiation control parameters based on the foot longitudinal force, to scale up its control efficiency. Finally, to ameliorate the issue of limited contact friction between the robot and the ground, an encoding approach to identify the sliding state of the robot's foot-end in the X-Y plane has been proposed. This approach adaptively modifies both the longitudinal force threshold of the foot-end and the desired linear velocity of the robot body. These adjustments are guided by specific control rules, which aim to enhance the accuracy of speed tracking during legged motion across varying terrains. The resulting linear and yaw velocity steady errors are constrained to 0.05 m/s and 0.012 rad/s, respectively. © 1996-2012 IEEE.

关键词： Anthropomorphic robots

Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE controllers Under External Uncertainties

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

Task allocation for multi-agent systems via unequal-dimensional optimal transport

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arXiv 2025年

作者： Dong, Anqi Johansson, Karl H. Karlsson, Johan Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Division of Decision and Control Systems and Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Mathematics and Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden

We consider a probabilistic model for large-scale task allocation problems for multi-agent systems, aiming to determine an optimal deployment strategy that minimizes the overall transport cost. Specifically, we assign transportation agents to delivery tasks with given pick-up and drop-off locations, pairing the spatial distribution of transport resources with the joint distribution of task origins and destinations. This aligns with the optimal mass transport framework where the problem and is in the unequal-dimensional setting. The task allocation problem can be thus seen as a linear programming problem that minimizes a quadratic transport cost functional, optimizing the energy of all transport units. The problem is motivated by time-sensitive medical deliveries using drones, such as emergency equipment and blood transport. In this paper, we establish the existence, uniqueness, and smoothness of the optimal solution, and illustrate its properties through numerical *** Codes 91B32, 93A16, 91B68, 90B06 © 2025, CC BY.

关键词： Linear programming

Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

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arXiv 2025年

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This paper focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyperparameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error