Path planning is a critical challenge for autonomous robots. Current algorithms lack environment-based directional guidance, often generating extensive low-quality path explorations that degrade both path quality and computational efficiency. To address this challenge, we propose the heuristic evaluation-driven tree search (HE-TS) algorithm, inspired by human navigation strategies and the principle of laser rangefinders. HE-TS performs tree search in the environment through a two-layer structure: waypoint extension and node expansion, balancing global exploration capability and local refinement for improved path quality. In the first layer of waypoint extension, HE-TS cast rays from the current position to acquire environmental information, enabling the identification of optimal extension directions for placing waypoints, thereby improving path quality. In the second layer, where node expansion is constructed from waypoint extensions, HE-TS defines waypoints with state changes as nodes and adopts state-aware node expansion to implement differentiated obstacle avoidance strategies, thus reducing the number of tree search branches. Simulation experiments in various environments demonstrate that HE-TS outperforms comparison algorithms in terms of path quality, strategy selection, and reduction of invalid extensions.

Modern railway trains raise higher demands on stability, operational efficiency, and comfort. To this end, distributed tracking control is vital to improve tracking performance and reduce the coupling force between traction units. This paper presents a novel adaptive cooperative tracking control approach for high-speed trains based on a multi-agent model. The proposed method ensures accurate tracking of both displacement and velocity, and effectively mitigates the coupling forces between adjacent traction units. Specifically, an improved multi-agent model of the high-speed train is developed, in which uncertain nonlinear resistance is approximated using an adaptive neural network, and the coupler connecting adjacent agents (traction units) is modeled as a spring-damper system. To formulate a refined controller, prior knowledge of the train system, such as the inherent resistance and coupling structure, is fully utilized so that only a few unknown parameters need to be estimated. Furthermore, an adaptive cooperative controller is designed to achieve accurate speed and displacement tracking. With this controller, multiple agents are simultaneously coordinated, while nonlinearities and uncertainties are handled. The coupling force between adjacent agents is effectively mitigated compared to traditional methods. Comparative simulation studies are conducted to demonstrate the effectiveness of the proposed adaptive cooperative tracking controller.

Distributed Kalman filtering is a widely used technique for state estimation in sensor networks system. Typically, each node utilizes the measurement residuals and the information exchanged with the neighbors to obtain the fused state estimation. However, local measurement is susceptible to outliers, and data sharing leads to privacy and security issues. In this paper, a secure and privacy-preserving distributed Kalman filter (SPP-DKF) is developed to simultaneously address the aforementioned issues. Specifically, the outlier-resistant mechanism is embedded to improve the resilience of the distributed Kalman filter, which employs the saturation function to suppress the mutation of innovation caused by outliers. An event-triggered privacy-preserving scheme based on dynamic mask is designed to protect privacy of local state against different adversaries. Meanwhile, to ensure security, the digital signature based on homomorphic encryption and hash function is adopted to detect false data injection attacks. Furthermore, the mean-square estimation performance and the upper bound of the error covariance of the proposed SPP-DKF are analyzed. In addition, the privacy and security performance of the proposed algorithm are also ensured. Finally, simulation results indicate that the proposed SPP-DKF provides reliable estimation performance while ensuring the privacy and security of the exchanged data.

High-speed railway operations are frequently disrupted by both external and internal factors. This study addresses train rescheduling and passenger reassignment in high-speed railway systems with seat reservation mechanisms by considering both operator and passenger perspectives and incorporating passenger compensation strategies to enhance service quality. The impacts of disruptions are quantified in terms of train delay costs and passenger compensation costs. An integrated mixed-integer programming (MIP) model is developed to minimize the weighted total operating cost, subject to constraints on train operations, station and track capacities, train capacities, and passenger boarding, transfer, and compensation rules. To solve the proposed model, a BD-RH computational framework is introduced by combining Benders decomposition (BD) and the rolling horizon (RH) algorithm, which decomposes the integrated problem into a train rescheduling master problem and a passenger reassignment subproblem for iterative optimization. Numerical experiments on both an artificial network and a subnetwork of China’s high-speed railway system validate the effectiveness and efficiency of the proposed approach. The findings provide actionable guidance for formulating passenger compensation strategies and passenger-oriented rescheduling strategies.

Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. This paper focuses on a real-time train timetable rescheduling problem in a railway system with a seat-reserved mechanism during disruptions. An integer linear programming model with a compensation mechanism is established to provide a better overall solution for dynamic and multistage rescheduling problems. The main part of the model considers the strategies of retiming, adding train stops and reordering to reduce the impact of passengers, while the compensation mechanism part mainly deals with the uncertainty of disruption durations. The related constraints mainly include section track capacity, station track capacity,passenger transfer rules and some robust measures under the uncertainty of disruption durations. A multistage decision optimization framework is established, and a rolling horizon algorithm is embedded in it to approximate the desired optimal solution. A series of numerical experiments based on a real case of China’s high-speed railway subnetwork are carried out to verify the effectiveness and efficiency of the proposed optimization approach.

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

Energy efficiency of wireless sensors is critical to maintaining the function of the monitoring system. Generally, the energy consumed in data transmission is much larger than in compression. Hence, decreasing data packet size with the aid of data compression before transmission can facilitate the reduction of energy consumption in communication. However, the energy consumed in data computation is also considerable, and improper computation ways may incur more energy consumption. To address this issue, in this article, two-hierarchy communication and computation hybrid optimization protocol is presented to minimize the total energy consumption. First, the cluster heads (CHs) rotation and clusters updating strategies are proposed in the communication layer, and the optimized adaptive compression ratios for the CHs are adopted in the computation layer. The hybrid optimization scheme is performed from the views of communication and computation synergistically to improve energy efficiency. The simulation results show the superiority of the proposed protocol compared with other outstanding protocols.

Automatic operation of high speed trains (HSTs) requires dedicated control schemes to tackle uncertain dynamics, unknown resistive forces, coupling nonlinearities, interactive in-train forces, unexpected disturbances, and faults. This paper addresses the problem of position and velocity tracking control of HSTs with multiple vehicles connected through elastic couplers. A neuro-adaptive fault tolerant control scheme is developed to compensate the input nonlinearities due to traction or braking notches, uncertain impacts from in-train forces, resistive aerodynamic drag forces, traction or braking faults, and adherence-antiskid constraints. High precision velocity and position tracking is achieved by using the proposed control scheme that combines the robust adaptive control with nonlinearly layered neural networks. Closed-loop stability is ensured with strict mathematical analysis. The effectiveness of the proposed approach is also validated through numerical simulations with considering the adherence-antiskid constraints.

The multi-hop communication protocol can balance the energy consumption of sensors to extend the service lifetime in high-speed railways (HSRs). However, the communication via multiple hops will increase the data transmission latency. Most previous studies have focused on optimizing either the sensor network lifetime or the data transmission latency but have not considered both. This paper presents an adaptive multi-objective optimization model for multi-hop communication systems. This model explicitly addresses the trade-off between the lifetime and the latency associated with the use of network-level wireless condition monitoring systems for ensuring the railway operational safety. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed approach. Compared with the three previously applied protocols, the proposed approach can achieve longer sensor network lifetime, shorter data latency, and greater system utility (accounting for both lifetime and latency). This paper provides the technical support for the development of stable and reliable wireless monitoring management systems for HSR safety.

Optimization of island microgrids should configure the module type and size in such a way that multiple objectives can be balanced. This paper presents a bioinspired optimization approach of microgrid sizing, with two salient features. First, the multiple objectives are categorized into four types: reliability, economy, renewable technology, and pollution. We present a triangular aggregation model, which is straightforward and cost effective to compute the fitness. Second, a bioinspired algorithm named Levy-Harmony is developed. We embed the Levy flight into the Harmony vector updating to enhance the global searching ability and, meantime, adopt a bias factor to avoid unnecessary exploration. The searching speed and accuracy are well balanced and improved. The real datasets are used for comparative studies, demonstrating the superiority of the proposed scheme against typical existing approaches.