Lateral Control in Precision Docking Using RTK-GNSS/INS and LiDAR for Localization

Ando, Takayuki; Kugimiya, Wataru; Hashimoto, Tatsuya; Momiyama, Fujio; Aoki, Keiiji; Nakano, Kimihiko

doi:10.1109/tiv.2020.2992857

Cited by 25 publications

(9 citation statements)

References 14 publications

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“…The hardware-in-the-loop test results show that the control method proposed in the paper improves the accuracy of vehicle path tracking control. 4. This method provides a new idea for studying the lateral control stability control of intelligent driving vehicles.…”

Section: Establishment Of a Vehicle Dynamics Model Vehicle Dynamics Tracking Error Modelmentioning

confidence: 99%

“…Intelligent driving vehicles are not only a research hotspot in the field of vehicle engineering but are also a new direction in the development of automobile industry for the future [1,2]. Vehicle motion control is the core to realize intelligent driving and lateral motion control is an important factor in ensuring vehicle driving safety, handling stability and other performance [3,4]. Its research has strong theoretical research significance and engineering application value [5].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Yang

Bai

et al. 2021

Actuators

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Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

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Section: Establishment Of a Vehicle Dynamics Model Vehicle Dynamics Tracking Error Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Yang

Bai

et al. 2021

Actuators

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show abstract

“…K is the Kalman gain, M u and M y are the fault direction matrices [48]. Nowadays, UAV formation (swarm) control practice uses the Real-time Kinematic Global Navigation Satellite System-Inertial Navigation System (RTK-GNSS-INS) [49] for routing and navigation. Moreover, inter-UAV sensors such as Automatic Dependent Surveillance-Broadcast (ADS-B) [50], which provides altitude, aircraft flight ID, and vertical airspeed, have been used in advanced systems.…”

Section: Sensor Magnitude Reconstructionmentioning

confidence: 99%

Safety Enhancement of UAVs from the Signal Processing’s Perspectives: A Bird’s Eye View

Kwan

2021

Drones

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Unmanned air vehicles (UAVs) or drones have gained popularity in recent years. However, the US Federal Aviation Administration (FAA) is still hesitant to open up the national air space (NAS) to UAVs due to safety concerns because UAVs have several orders of magnitude of more accidents than manned aircraft. To limit the scope in this paper, we focus on large, heavy, and expensive UAVs that can be used for cargo transfer and search and rescue operations, not small radio-controlled toy drones. We first present a general architecture for enhancing the safety of UAVs. We then illustrate how signal processing technologies can help enhance the safety of UAVs. In particular, we provide a bird’s eye view of the application of signal processing algorithms on condition-based maintenance, structural health monitoring, fault diagnostics, and fault mitigation, which all play critical roles in UAV safety. Some practical applications are used to illustrate the importance of the various algorithms.

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“…For localization and navigation, global navigation satellite system (GNSS) are probably one of the most popular methods currently available. Using satellite localization and combining it with an inertial navigation system (INS) can enable intelligent vehicles to obtain high localization accuracy and accurate navigation routes and is currently widely used in maps, such as Google, Gaode, and Baidu [3]. However, in underground parking lots, the closed nature of the environment results in the unavailability of GNSS signals, and the use of INS alone can result in many accumulated errors, leading to the failure of localization and navigation.…”

Section: Introductionmentioning

confidence: 99%

Intelligent vehicle localization and navigation based on intersection fingerprint roadmap (IRM) in underground parking lots

Li,

Yang,

Cai

et al. 2023

Meas. Sci. Technol.

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The localization and navigation of underground parking lots presents a ‘last mile’ problem for intelligent vehicles. To address the issues of poor localization and navigation efficiency caused by the lack of global navigation satellite system signals in this scenario, this study presents a low-cost and computationally efficient real-time localization and navigation algorithm. The algorithm begins by constructing a node fingerprint based on the characteristics of underground parking lot intersections and proposes an intersection fingerprint roadmap (IRM). To address the problem of difficult initial position computation during navigation, a node-level localization method based on scene matching and pose calculation is proposed and then combined with the IRM. Finally, the IRM is used to search for routes on the scene plan and perform path smoothing to obtain a globally feasible driving route. The algorithm proposed in this study can achieve fast vehicle localization and real-time navigation with low computational power, and the proposed localization method can be applied not only to underground parking lots but also to other outdoor scenes. The method is evaluated on datasets collected from different environments, and the experimental results show that the algorithm has an average localization error of approximately 20 cm and provides faster navigation speed and smoother navigation in comparison with other algorithms.

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Lateral Control in Precision Docking Using RTK-GNSS/INS and LiDAR for Localization

Cited by 25 publications

References 14 publications

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Safety Enhancement of UAVs from the Signal Processing’s Perspectives: A Bird’s Eye View

Intelligent vehicle localization and navigation based on intersection fingerprint roadmap (IRM) in underground parking lots

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