A review for control theory and condition monitoring on construction robots

Shi, Huaitao; Li, Ranran; Bai, Xiaotian; Zhang, Yixing; Min, Linggang; Wang, Dong; Lu, Xinyu; Yan, Yang; Lei, Yaguo

doi:10.1002/rob.22156

Cited by 32 publications

(9 citation statements)

References 132 publications

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“…One of the early works, RailSLAM, jointly estimated the train state and validated the correctness of the initial track map based on a general Bayesian theory (Heirich et al, 2013). The performance of visual-inertial odometry (VIO) on rail vehicles has been extensively evaluated by Etxeberria-Garcia et al (2021) and Tschopp et al (2019), indicating that the VIO is not reliable for safety critical applications, especially at challenging scenarios with high speeds, repetitive patterns, and unfavorable illumination conditions (Shi et al, 2023).…”

Section: Train Localization and Mappingmentioning

confidence: 99%

Four years of multimodal odometry and mapping on the rail vehicles

Wang,

Song,

Zhang

et al. 2023

Journal of Field Robotics

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Precise, seamless, and efficient train localization as well as long‐term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety engineering for railroad systems. Simultaneous localization and mapping is right at the core of solving the two problems concurrently. To this end, we propose a high‐performance and versatile multimodal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial‐centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation, and map‐based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors inertial measurement unit and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared with point‐only LiDAR‐inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The visual‐inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long‐during railway environments over 4 years, including general‐speed, high‐speed, and metro, where both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experiences, problems, and successes of our group with the robotics community so that those who work in such environments can avoid these errors. In this view, we open source some of the data sets to benefit the research community.

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Section: Train Localization and Mappingmentioning

confidence: 99%

Four years of multimodal odometry and mapping on the rail vehicles

Wang,

Song,

Zhang

et al. 2023

Journal of Field Robotics

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“…In the construction of fast-paced and high-efficiency buildings, the construction efficiency and work quality of masonry wall are crucial to the whole duration and building quality. At present, the construction of masonry wall is mainly by construction workers hand-built, labor intensity, low efficiency [1][2][3] . When the height of masonry is high, construction workers need to work at high altitude through other auxiliary tools, there is a potential safety hazard.…”

Section: Introductionmentioning

confidence: 99%

Servo motor fault detection for construction robot base on observer

jinbao,

ke,

huaitao

et al. 2023

Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023)

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In this paper, a fault diagnosis scheme based on observer is proposed to solve the problem of servo motor fault in construction robot actuator system. Firstly, the mathematical model is established in abc coordinate system, and Park transformation is introduced to transform abc three-phase symmetry variable into dq coordinate DC through Park transformation, which greatly reduces the modeling complexity in normal state of motor, eliminate redundant motor models. Based on this, the state space equations are constructed and an interval observer is designed. Compared with the traditional Luenberger interval observer, the interval observer proposed in this paper has better decoupling performance in solving the gain matrix, at the same time, it directly improves the robustness of the interval observer and the sensitivity to the fault signal under the electromagnetic interference. Finally, the method is verified on the fault simulation test-bed, which proves the feasibility of the proposed method, and further improves the timeliness of fault detection of servo motor in construction robot actuator system.

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“…As a large material transportation equipment, automated lifting robot has high transportation efficiency, wide application range and low cost. It is widely utilized in various industries, including hydroelectric power plants, shipyards, and freight ports, for the precise and effective transportation of materials (Shi et al, 2023; Sun et al, 2019; Tan et al, 2021). However, given the complicated working environment of automated lifting robots, there will unavoidably be obstacles (e.g., containers) in the movement path, which are prone to causing major safety accidents if not probably controlled (Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

Tan

Zhang

Shi

et al. 2023

Journal of Field Robotics

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The existing automated lifting robot technology focuses merely on motion control and ignores the surrounding environment. In practice, obstacles inevitably exist in the movement path of the automated lifting robot, which affects construction safety. Furthermore, due to the underactuated characteristics of the automated lifting robot, the load can be difficult to control when it swings violently, which undoubtedly poses huge challenges to obstacle avoidance trajectory planning and controller design. In this paper, an obstacle avoidance trajectory and its tracking controller with antiswing and tracking errors constraint are proposed. To ensure accurate load positioning and effective obstacle avoidance, the proposed control method introduces a four-segment polynomial trajectory interpolation curve to construct an obstacle avoidance trajectory based on analyzing the geometric relationship between variables. To improve the transient coupling control performance of the system, combined with the passive analysis of the automated lifting robot system, this method constructs a potential function that limits the tracking error and a coupling signal that enhances the coupling relationship between the system variables. Barbalat's lemma and Lyapunov techniques are used to analyze the stability of the system. Simulation and experimental results show that the proposed control method can significantly suppress or even eliminate load oscillation, accurately locate the load, avoid obstacles, improve the safety and efficiency of the working automated lifting robot, and have strong robustness to changes in system parameters and the addition of external disturbances.

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A review for control theory and condition monitoring on construction robots

Cited by 32 publications

References 132 publications

Four years of multimodal odometry and mapping on the rail vehicles

Four years of multimodal odometry and mapping on the rail vehicles

Servo motor fault detection for construction robot base on observer

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

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