Search results

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

The purpose of this study is to reduce the residual stress in welded workpieces, optimize the vibratory stress relief treatment process through the use of a vibration generator and enhance the durability and longevity of the workpiece by developing a vibratory stress relief robot that incorporates a multi-manipulator system.

The multi-manipulator combination work is designed so that each manipulator is deployed according to the requirements of vibration stress relief work. Each manipulator works independently and coordinates with others to achieve multi-dimensional vibratory stress relief of the workpiece. A two-degree-of-freedom mobile platform is designed to enable the transverse and longitudinal movement of the manipulator, expanding the working space of the robot. A small electromagnetic superharmonic vibration generator is designed to produce directional vibrations in any orientation. This design addresses the technical challenge of traditional vibration generators being bulky and unable to achieve directional vibrations.

The residual stress relief experiment demonstrates that the residual stress of the workpiece is reduced by approximately 73% through three-degree-of-freedom vibration. The multi-dimensional vibration effectively enhances the relief effect of residual stress, which is beneficial for improving the strength and service life of the workpiece.

A new multi-manipulator robot is proposed to alleviate the residual stress generated by workpiece welding by integrating vibratory stress relief with robotics. It is beneficial to reduce material and energy consumption while enhancing the strength and service life of the workpiece.

Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop.

In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching.

Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization.

The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

Regular cable trench inspection is crucial, and robotics automation provides an efficient and safer alternative to manual labor. However, existing robots have limited capabilities in traversing obstacles and lack a mechanical arm for detecting cables and equipment. This study aims to develop an intelligent robot for cable trench inspection, enhancing obstacle-crossing abilities and incorporating a mechanical arm for inspection tasks.

This study presents an intelligent robot for cable trench inspection, featuring a six-degree-of-freedom mechanical arm mounted on a six-track chassis with four flippers. The robot's climbing and obstacle-crossing stability, as well as the motion range of the mechanical arm, are analyzed. The positioning, navigation and remote monitoring systems are developed. Experiments, including climbing and obstacle-crossing performance tests, along with navigation and positioning system tests, are conducted. Finally, the robot's practicability is verified through field testing.

Equipped with flipper tracks, the cable trench inspection robot can traverse obstacles up to 30 cm high and maintain stable locomotion on 30° slopes. Its navigation system enables autonomous operation, while the mechanical arm performs cable current detection tasks. The remote monitoring system provides comprehensive control of the robot and environmental parameter monitoring in cable trenches.

The front and rear flipper tracks enhance the robot's ability to traverse obstacles in cable trenches. The mechanical arm addresses cable current and equipment contact detection issues. The navigation and remote monitoring systems improve the robot's autonomous operation and environmental monitoring capabilities. Implementing this robot can advance the automation and intelligence of cable trench inspections.

Indoor hallways are the most common and indispensable part of people’s daily life, commercial and industrial activities. This paper aims to achieve high-precision and dense 3D reconstruction of the narrow and long indoor hallway and proposes a 3D, dense 3D reconstruction, indoor hallway, rotating LiDAR reconstruction system based on rotating LiDAR.

This paper develops an orthogonal biaxial rotating LiDAR sensing device for low texture and narrow structures in hallways, which can capture panoramic point clouds containing rich features. A discrete interval scanning method is proposed considering the characteristics of the indoor hallway environment and rotating LiDAR. Considering the error model of LiDAR, this paper proposes a confidence-based point cloud fusion method to improve reconstruction accuracy.

In two different indoor hallway environments, the 3D reconstruction system proposed in this paper can obtain high-precision and dense reconstruction models. Meanwhile, the confidence-based point cloud fusion algorithm has been proven to improve the accuracy of 3D reconstruction.

A 3D reconstruction system was designed to obtain a high-precision and dense indoor hallway environment model. A discrete interval scanning method suitable for rotating LiDAR and hallway environments was proposed. A confidence-based point cloud fusion algorithm was designed to improve the accuracy of LiDAR 3D reconstruction. The entire system showed satisfactory performance in experiments.

This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.

This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.

The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.

This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.

本文旨在系统回顾人工智能（AI）在旅游业中的应用。通过整合人机交互、机器学习、大数据和其他相关技术, 本研究建立了一个全面的研究框架, 探索人工智能与旅游业各方面之间的系统联系。

本文对4048篇与旅游业人工智能相关的文章进行了关键词共现分析。分析确定了主要议题并对其进行了分类, 然后通过主题文献梳理和手动编码对其进行了进一步完善, 以便进行详细讨论。

分析揭示了五个主要主题：人工智能与旅游体验、人工智能与旅游营销和预测、人工智能与目的地管理、人工智能与旅游企业, 以及人工智能在战略和监管框架中的整合。每个主题都进行了回顾, 以构建一个综合讨论, 勾勒出当前的研究格局, 并提出了未来的研究方向。

研究力图突破目前关于旅游与人工智能的碎片化讨论, 建立了一个统一的框架, 旨在加强对旅游业中人工智能现有方法、理论和应用的理解, 还点明了需要突破的关键领域, 以助力旅游业与人工智能共同创造更加人性化和可持续发展的前景。

Este artículo pretende revisar sistemáticamente la aplicación de la inteligencia artificial (IA) en el sector turístico. Mediante la integración de la interacción humano-ordenador, el aprendizaje automático, big data y otras tecnologías relevantes, el estudio establece un marco de investigación exhaustivo que explora las conexiones sistemáticas entre la IA y diversas facetas del turismo.

Este trabajo realiza un análisis de co-ocurrencia de palabras clave de 4.048 artículos relacionados con la IA en el turismo. El análisis identifica y clasifica los temas dominantes, sobre los que se profundiza mediante una revisión temática de la literatura y una codificación manual para su discusión detallada.

El análisis presenta cinco temas principales: El impacto de la IA en la experiencia turística, la IA en el marketing y la predicción turística, la IA en la gestión de destinos, el papel de la IA en las empresas turísticas y la integración de la IA en el marco estratégico y normativo. Cada tema se revisa para construir un debate integrado que trace el panorama actual y sugiera direcciones para futuras investigaciones.

Este artículo expande el análisis fragmentado que suele encontrarse en la bibliografía al establecer un marco unificado que no sólo mejora la comprensión de las metodologías, teorías y aplicaciones existentes de la IA en el turismo, sino que también identifica las áreas críticas para los avances, con el objetivo de inspirar una integración más humana y sostenible de la IA en la industria turística.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

This study aims to improve the stability and obstacle surmounting ability of the traditional wall-climbing robot on the surface of the ship, a wheel-track composite magnetic adsorption wall-climbing robot is proposed in this paper.

The robot adopts a front and rear obstacle-crossing mechanism to achieve a smooth crossover. The robot is composed of two passive obstacle-crossing mechanisms and a frame, which is composed of two obstacle-crossing magnetic wheels and a set of tracks. The obstacle-crossing is realized by the telescopic expansion of the obstacle-crossing mechanism. Three static failure models are established to determine the minimum adsorption force for the robot to achieve stable motion. The Halbach array is used to construct the track magnetic circuit, and the influence of gap, contact area and magnet thickness on the adsorption force is analyzed by parameter simulation.

The prototype was designed and manufactured by the authors for static failure and obstacle crossing tests. The prototype test results show that the robot can cross the obstacle of 10 mm height under the condition of 20 kg load.

A new structure of wall-climbing robot is proposed and verified. According to the test results, the wall-climbing robot can stably climb over the obstacle of 10 mm height under the condition of 20 kg load, which provides a new idea for future robot design.