Trajectory Generation and Tracking Control of an Autonomous Vehicle Based on Artificial Potential Field and optimized Backstepping Controller

Sabiha, Ahmed D.; Said, Ehab; Kamel, Mohamed A.; Hussein, Walid

doi:10.1109/iceeng45378.2020.9171708

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…This section presents simulation results to highlight the performance of the proposed path following the control protocol. The AGV kinematic model is obtained from [16,30]. The initial conditions of the AGV are set as x n (0) = −0.5, y n (0) = 0.5, ẋn (0) = 0.1, y n (0) = 0.1, and ψ(0) = 0.1.…”

Section: Simulation Resultsmentioning

confidence: 99%

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Maaruf,

Mysorewala

2024

Preprint

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This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

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Section: Simulation Resultsmentioning

confidence: 99%

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Maaruf,

Mysorewala

2024

Preprint

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“…Te artifcial potential feld method is a kind of virtual force method [29][30][31]. Te premise of establishing a repulsion force feld is that the ego vehicle will purposefully avoid obstacle vehicles.…”

Section: Monocular-binocular Vision Switching Strategy Based Onmentioning

confidence: 99%

Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

Wang

Li-ming

Zhu

et al. 2023

Journal of Advanced Transportation

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This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.

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“…The basic concept of classical approaches is either to discover a feasible solution or to confirm that there is no solution. The main classical approaches are: cell decomposition [5,6], roadmap [7], sampling-based algorithms [8], and artificial potential field (APF) [9,10]. These methods are not usually mutually incompatible, and mostly a hybrid algorithm to contain two classical techniques is applied to improve the path planning process [11].…”

Section: Introductionmentioning

confidence: 99%

Real-time path planning for autonomous vehicle based on teaching–learning-based optimization

Sabiha

Kamel

Said

et al. 2022

Intel Serv Robotics

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This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.

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Trajectory Generation and Tracking Control of an Autonomous Vehicle Based on Artificial Potential Field and optimized Backstepping Controller

Cited by 13 publications

References 23 publications

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

Real-time path planning for autonomous vehicle based on teaching–learning-based optimization

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