Given the urgency and timeliness of the emergency response to fire, firefighting robots often struggle to locate the fire source accurately in a limited time and work efficiently to suppress fire. This article proposes finite-time fire positioning and fire extinguishing circumnavigation control algorithms for multiple intelligent firefighting robots (IFRs) based on bearing measurements, enabling them to quickly locate and efficiently extinguish the fire. First, a finite-time localization estimator is designed to enable all IFRs to quickly and precisely locate the fire, which utilizes the known positions of all IFRs and bearing measurements of the target fire. Furthermore, with the estimated fire position and change, a distributed circumnavigation control protocol is proposed for IFRs to perform all-round fire suppression and extinguishing. This protocol aims to drive the IFRs along dynamically varying circular trajectories with the desired enclosing distance, angular spacing, and tangential enclosing speed around the fire. Finally, numerical simulations and physical experiments are carried out to demonstrate the effectiveness of the proposed algorithms.

This article investigates the formation switching problem of a group of autonomous tracked mobile robots (ATMRs) traveling in unknown obstacle environments. Firstly, a structural model based on virtual leader-leader-follower is designed, where the leader tracks a reference trajectory determined by the virtual leader, while the followers track their desired positions dictated by the virtual leader. Subsequently, a dynamic optimization formation switching strategy is developed, in which the leader real-time calculates a formation adjustment factor to dynamically adjust the formation parameters according to the prevailing environmental constraints. Furthermore, a novel comprehensive performance evaluation index system is presented to assess formation switching efficiency, leveraging the inherent simplicity of the ATMR’s kinematics. Finally, extensive numerical simulations and physical experiments are carried out to validate the effectiveness of the proposed strategies.

共同配送是解决快递末端配送资源浪费严重、服务质量低劣等问题的有效措施.由于我国的快递共同配送研究起步较晚、经验缺乏等原因,其应用率并不高,且形成的共同配送联盟难以保持长期稳定的合作关系.为探究提升多方共同配送合作稳定性的有利条件,本文构建了三方参与的快递末端共同配送联盟演化博弈模型,以企业投入等因素为依据,将联盟成员按照规模大小划分为三类,分析了不同规模的企业在一定条件下共同配送合作过程中的策略选择,并通过仿真模拟的方式确定了三方趋于合作的条件.结果表明:初始合作意愿、收益分配占比、惩罚获利以及企业间的依赖度对联盟成员的策略选择以及联盟的稳定性有着重要影响.企业规模不同,对影响因素的敏感程度亦不同.

为了研究轮式移动机器人运动过程中万向轮扭转的扰动对其轨迹跟踪的影响,提出了 一种考虑前端万向轮摩擦扰动及其质心与几何中心不重合的轨迹跟踪控制方法.首先,建立了考虑万向轮扭转角扰动的轮式移动机器人动力学模型;其次,根据位姿误差模型,采用反步法设计虚拟速度控制器收敛系统位姿误差;然后,结合扰动观测器对机器人行进过程中万向轮扭转所带来的摩擦扰动进行估计,构造出一种基于积分滑模思想的力矩控制器以保证速度追踪;最后,利用Lyapunov稳定性理论对系统的稳定性和渐近收敛进行证明.仿真及实验结果表明,将万向轮摩擦所带来的扰动反馈给动力学控制器,可以减轻控制器的负载.与忽略万向轮扰动的控制方法相比,轮式移动机器人的位置误差最大值的均方根值降低了 37.37％,提高了运动系统的稳定性.

This paper investigates the cooperative control problem for a group of autonomous nonholonomic mobile robots, in which the robots are required to collaboratively enclose and track a stationary or moving target in a circular formation. In order to solve the challenging problem that the robots with speed constraints move uniformly to the exact position on the circles centered on the target while avoiding obstacles encountered, a distributed coupling controller scheme consisting of target encircling, phase positioning and spacing assignment, and the avoidance of obstacles is proposed. First, a novel circular motion control law based on the feedback control idea of trajectory tracking is proposed, which guides all robots move to the target-centered circles and maintains the expected distances between the robots and the target. Second, a phase positioning and spacing assignment control law by introducing a nonlinear function is proposed, which can be coupled into the circular motion controller to implement the robots converge to the specified position on the circles. Finally, the obstacles avoidance control law based on artificial potential field only with repulsive force is adopted to ensure each robot effectively avoids obstacles. The rigorous theoretical analysis of the convergence of the proposed controller is given, and then the simulations and experiments are provided to validate the effectiveness and applicability of the proposed control scheme.

近年来,共享合作开始成为快递行业发展的新方向.本文以共同配送中心雇佣快递员为快递企业提供城市末端配送服务且快递员可自主选择配送方式为共同配送场景,考虑历史平均月派件量、行业平均月工资水平、共同配送中心的配送信用和效率等因素对收益的影响,构建快递企业、共同配送中心、快递员以及快递柜和驿站经营企业的3级Stackelberg动态博弈模型,使用逆向推导法研究最佳定价策略,并结合实际数据分析模型的有效性.研究发现:共同配送联盟为快递企业提供统一的末端配送服务,能降低快递企业的配送成本;此外,制定合理的定价策略是维系联盟稳定发展、激发联盟活力的重要手段.

目前,共同配送模式在快递行业的实际利用率不高,存在合作范围较小、定价机制模糊、收益分配不合理的问题.以共同配送为研究背景,基于快递员行为对快递企业收益的影响,构建快递企业、共配中心、承包区、快递员、快递柜(驿站)经营企业的4级Stackelberg定价博弈模型,基于逆向推导法制定最佳定价策略,并结合实际运营数据分析模型有效性.研究发现:共同配送联盟能有效降低快递企业的配送成本,提升企业效益;合理的定价机制能为各参与方提供稳定收益,维持联盟发展;快递员行为是影响联盟定价的重要因素.

为解决视觉SLAM(同时定位与地图创建)算法依赖图像亮度而对光照变化场景敏感的问题,提出一种基于在线光度标定的半直接视觉SLAM算法.首先,根据相机成像原理,提出基于光度标定的帧间位姿估计方法,在求解位姿的同时对原始的输入图像进行光度校正.其次,在特征追踪环节采取最近共视关键帧匹配策略,以提升特征点匹配效率.最后,对后端重投影迭代优化策略进行改进,降低光照变化对视觉SLAM算法的精度和鲁棒性的影响.在TUM、EuRoC数据集上的实验结果表明,本算法的轨迹估计精度优于LSD-SLAM和SVO 2.0算法,尤其是在中等难度、高难度的数据集序列上.在真实环境测试中,通过对比本算法与激光方法的轨迹估计结果,证明本算法有效提高了传统视觉SLAM方法在光照不均匀场景下的定位精度与鲁棒性.

In this article, a robust discrete-time open-closed-loop proportion integral differential (PID) -type iteration learning control (ILC) algorithm is developed for the high-precision trajectory tracking control of tracked mobile robots (TMRs) with external disturbances and noises. The proposed ILC algorithm adopts the past, current, and predictive learning error items of the former and current iterations to correct the current control input variables, which finally converges to the desired trajectory through continuous iterative learning. The convergence characterization of the algorithm for TMRs under both external disturbances and noises is carried on rigorous mathematical proof. Numerical simulations and physical experiments are provided to verify the feasibility and effectiveness of the algorithm. The comparative results of two ILC algorithms indicate that the tracking performance of the proposed ILC algorithm is superior to the traditional PID-type ILC algorithm in terms of tracking accuracy and convergence rate.

针对转运合作的新模式——共享转运中心,基于动态博弈理论构建共享转运服务动态定价模型,并分析模型的复杂动力学特征;通过数值仿真重点讨论价格调整系数对系统稳定性和利润的影响,并结合实际需求和是否提供历史收件价格、服务价格以及动态博弈定价模型设计共享转运服务动态定价系统的技术路线及框架、类关系以及实现动态定价的业务逻辑;最后利用Python编程语言的Django框架,开发互联网+快递的新应用——共享转运动态定价系统.

Visual sorting of stacked parcels is a key issue in intelligent logistics sorting systems. In order to improve the sorting success rate of express parcels and effectively obtain the sorting order of express parcels, a visual sorting method based on multi-modal information fusion (VS-MF) is proposed in this paper. Firstly, an object detection network based on multi-modal information fusion (OD-MF) is proposed. The global gradient feature is extracted from depth information as a self-attention module. More spatial features are learned by the network, and the detection accuracy is improved significantly. Secondly, a multi-modal segmentation network based on Swin Transformer (MS-ST) is proposed to detect the optimal sorting positions and poses of parcels. More fine-grained information of the sorting parcels and the relationships between them are gained by adding Swin Transformer models. Frequency domain information and depth information are used as supervision signals to obtain the pickable areas and infer the occlusion degrees of parcels. A strategy for the optimal sorting order is also proposed to ensure the stability of the system. Finally, a sorting system with a 6-DOF robot is constructed to complete the sorting task of stacked parcels. The accuracy and stability the system are verified by sorting experiments.

Automated guided vehicles (AGVs) are widely used in various material handling systems due to their high efficiency and low cost, particularly in warehouses. However, deadlock resolution among vehicles is still a thorny issue. Many algorithms re-plan motion paths to avoid deadlocks resulting in greatly increasing the computational complexity, while other deadlock resolution algorithms in which vehicles have fixed paths are less efficient. This paper proposes a spare zone based hierarchical (SZH) motion coordination algorithm supporting time-efficient collision-and deadlock resolution among vehicles. Unlike previous deadlock resolution methods in which the vehicles can change their paths to avoid deadlocks at any time, SZH avoids deadlocks just by locally adjusting their paths in a decentralized manner. The introduction of a spare zone mechanism ensures reliable resolution of different types of collision and deadlocks while achieving high time efficiency. The allocation of spare zones for each vehicle before performing their missions guarantees that there exist enough free zones to which the vehicles taking part in deadlocks can temporary departs from their current path to avoid deadlocks. In order to improve the efficiency, the vehicle?s spare zones are updated as soon as the vehicle arrives at a zone. The algorithm is realized based on a hierarchical strategy, where the top layer considers the allocation and update of spare zones, while the bottom layer considers the coordination among vehicles within two zones. Extensive simulation results demonstrate the feasibility and efficiency of the proposed motion coordination algorithm.

针对低成本惯性测量器件采用基于滤波的姿态解算算法存在精度低、抗干扰性差等问题,提出了一种基于共轭梯度法与互补滤波相融合的自适应参数调节的混合滤波算法.该算法首先利用共轭梯度算法对加速度计和磁力计的数据进行姿态四元数的迭代估算,再通过互补滤波算法将陀螺仪更新的姿态与其进行信息融合,最后根据载体的运动状态自适应调节滤波参数,实现最优姿态估计.为验证所提算法的可行性和抗干扰性,与其他滤波融合算法在移动机器人平台上进行抗磁干扰和抗运动加速度干扰实验.实验结果表明,该算法可以有效地降低磁干扰和运动加速度干扰对姿态角解算的影响,其姿态角解算精度优于传统的梯度下降法、高斯牛顿法和共轭梯度法的滤波融合算法.

针对大规模环境下传统A*算法路径寻优存在的内存占有率高、计算效率低下的问题,提出了一种改进A*算法.引入了双向搜索机制,以原始起点、终点和对向搜索所处的当前节点作为目标点进行搜索操作,使AGV的路径寻优具备更加合理的方向性;优化评价函数,改进了评价函数的传统计算方式,通过测试为评价函数选择了合适的权重系数,减少路径寻优过程中的冗余点,提升路径寻优的计算效率,节约内存占有率.为了验证改进A*算法的有效性,在Matlab平台中进行编程,在不同尺寸的含障碍栅格地图中进行了仿真.仿真结果表明:改进A*算法在路径寻优过程中所遍历的节点数量较少,搜索过程中的计算效率更高,并且可获得到达目标点的最短路径.

X-ray imaging can be used to inspect the internal structure of the objects without destruction, so visual inspection based on X-ray images is widely used in the security check such as customs, airports, railway stations, and postal express. Especially in the postal express industry, fast and accurate inspection of express parcels can effectively improve logistics efficiency. This article studies the application of computer vision technology to detect prohibited items in X-ray images. Due to the multi-pose objects in the packages under multi-views, it is difficult to find out the prohibited item from the packages under a single view. This article explores how to solve this problem with the loss function of classification and the attention mechanism of convolutional neural network, and apply them to high-efficiency detectors. On the one hand, we proposed a new loss function named truncated loss for X-ray image classification task. In the proposed loss, we truncated input vector of loss layer to reduce the difference within the intra-classes and increase the difference between the inter-classes. On the other hand, we proposed two new architectures for the high-efficiency detectors for the purpose of obtaining the visual features of prohibited item more effectively. One of the new architectures named channel context block (CC block), and it is based on global context (GC block). It contains global context information on each channel through operations of global average pooling, which is different from global context (GC) block. The other one of the architectures named GCC block, it is formed by merging channel context block (CC block) and global context (GC) block, and it is used to further improve the detection accuracy of prohibited item. The results of experiments on the currently widely used high-efficiency detectors in GDXray dataset show that our proposed truncated loss can improve the detection accuracy of prohibited item to a certain extent, and the new architectures can improve detection accuracy to a greater extent. The algorithms proposed in this article are also state-of-the-art on GDXray dataset.

Visual sorting of express parcels in complex scenes has always been a key issue in intelligent logistics sorting systems. With existing methods, it is still difficult to achieve fast and accurate sorting of disorderly stacked parcels. In order to achieve accurate detection and efficient sorting of disorderly stacked express parcels, we propose a robot sorting method based on multi-task deep learning. Firstly, a lightweight object detection network model is proposed to improve the real-time performance of the system. A scale variable and the joint weights of the network are used to sparsify the model and automatically identify unimportant channels. Pruning strategies are used to reduce the model size and increase the speed of detection without losing accuracy. Then, an optimal sorting position and pose estimation network model based on multi-task deep learning is proposed. Using an end-to-end network structure, the optimal sorting positions and poses of express parcels are estimated in real time by combining pose and position information for joint training. It is proved that this model can further improve the sorting accuracy. Finally, the accuracy and real-time performance of this method are verified by robotic sorting experiments.

Automated guided vehicles (AGVs) are widely used for material handling in warehouses and automated production lines due to their high efficiency and low cost. However, AGVs usually interact with each other because of the restricted capacity of the layout. Although many algorithms have been proposed to address the problem, most of them are inefficient for collision and deadlock avoidance in dynamic environments. This paper proposes a dynamic resource reservation (DRR) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs. In this method, the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph. In order to solve the collision and deadlock problem dynamically, the shared resource points of each vehicle are extracted from their guide paths in real time. Unlike the traditional approaches most of which adopt a static point occupation policy, DRR exploits dynamical reservations of shared resource points to change AGV movement states for avoiding collisions and deadlocks, resulting in better time efficiency. We jointly implement the algorithm on both central and local controllers. Extensive simulation results demonstrate the feasibility and efficiency of the proposed collision and deadlock prevention method.

Wireless sensor networks (WSNs) are distributed networking systems consisting of many self-organized sensor nodes deployed in monitoring areas of interest. Emergency navigation is an emerging application of WSNs, which aims to solve the problem that users choose the optimal path and reach the designated target safely based on the sensed information in dangerous environments. However, traditional distributed emergency navigation algorithm has relatively low dynamic adaptability and navigation efficiency. In this paper, a dynamic emergency navigation algorithm based on prediction via WSNs is proposed. First, we introduce a prediction model based on time series to predict the dynamic changes of the environment sensed by WSNs. Then, for each user requesting the navigation path, the potential field of each node is established by comprehensively considering its distance to the target and corresponding predictive danger value. Based on the dynamically updated potential field, the users exploit the gradient descent method to efficiently approach the target areas node by node. The simulation results demonstrate the superiority of the proposed algorithm in terms of the navigation efficiency and safety in dynamic environments.

传统移动机械臂路径规划算法没有根据抓取点分布情况对工位点坐标进行优化,效率低,对此,提出了一种基于改进遗传算法的移动机械臂拣选路径优化方法.通过对拣选物品位置的分析,建立单个工位点上移动机械臂分拣路径模型和多工位点的旅行商(TSP)问题模型,运用改进的遗传算法,在工作空间内对各个工位点的位置坐标寻优,规划出移动机械臂抓取的最短路径和多工位点间移动的最短路径.实验结果表明,与传统遗传算法可能,运用改进的等级进化选择算子和最优近邻交叉算子,遗传算法的收敛速度提高了46.15％,路径缩短了45.99％,系统运行时间减少了25.80％,提高了系统效率.

根据"协同育人"人才培养的教育理念,结合邮政行业人才培养的特点,以建构主义学习理论为指导,将知识融合背景下研究协同育人的人才培养模式导入高校邮政人才培养的策略,探索多元协同育人模式,强化比较优势,开展项目共建、成果共享的人才培养机制,以改善目前高校人才培养的效果,实现邮政行业人才培养模式的创新.