Hierarchical Parking Path Planning Based on Optimal Parking Positions

Zhang, Yuegang; Chen, Guoying; Hu, Hongyu; Gao, Zhenhai

doi:10.1007/s42154-022-00214-z

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The difference is that A * algorithm adds all the unreachable neighbor nodes of the current node to the openlist, while JPS uses some methods to add "valuable" nodes to the openlist. [8]…”

Section: Jps Algorithmmentioning

confidence: 99%

Unmanned cart path planning based on improved A* algorithm

Zhang,

Ren

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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With the maturity of robotics, manual inspection is gradually replaced by inspection robots in large storage centres and other places where goods are densely packed. Aiming at the problems of low efficiency, high number of inflection points and inability to perform real-time obstacle avoidance in the traditional A* algorithm for mobile robot path planning, this paper proposes an improved A* search algorithm. Measures such as modification of the heuristic function, optimisation of the openlist list and curve smoothing are used to effectively improve the search efficiency.

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“…The difference is that A * algorithm adds all the unreachable neighbor nodes of the current node to the openlist, while JPS uses some methods to add "valuable" nodes to the openlist. [8]…”

Section: Jps Algorithmmentioning

confidence: 99%

Unmanned cart path planning based on improved A* algorithm

Zhang,

Ren

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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“…θ 2 cos θ 2 (11) Bringing Equation (10) into Equation (5) and simplifying it gives the yaw rate of the tractor as follows:…”

Section: Vehicle Kinematic Constraintmentioning

confidence: 99%

“…The search-based path planning algorithm [8] searches for the shortest path from the start to the endpoint by traversing neighboring nodes on a raster map to obtain the optimal global solution. However, the A* algorithm [9,10] and Dijkstra's algorithm need help planning feasible trajectories in complex scenarios. Additionally, the above two algorithms do not take into account the kinematic characteristics of the vehicle when planning trajectories, which cannot guarantee the feasibility of the planned trajectories in terms of control [11].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Wang,

Li,

Liu

et al. 2023

Applied Sciences

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With the development of science and technology, self-driving technology is gradually being applied to automobile semi-trailer trains. Aiming at the problem that it is challenging to plan the traveling trajectory of an autonomous semi-trailer train, a trajectory planning algorithm based on a constrained iterative linear quadratic regulator is proposed. The constrained iterative linear quadratic regulator solves the problem that the iterative constrained linear quadratic regulator cannot deal with inequality constraints by transforming inequality constraints into interior point penalty functions to deal with all kinds of inequality constraints in the trajectory planning problem. When dealing with the obstacle avoidance problem, the body contour is modeled approximately according to the actual shape of the vehicle, and the obstacle avoidance constraints are transformed into inequality constraints. Considering the unique body structure of a semi-trailer train, the articulation angle constraint function is designed according to different vehicle speeds. Simulation experimental results show that the algorithm can plan safe driving trajectories for semi-trailer trains in complex traffic scenarios.

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“…(1) The geometric method of arc plus straight line is used to construct the path, but the curvature of the path constructed by this method is not continuous, which will lead to the problem of in-situ steering of the vehicle during path tracking, and the method is more stringent on the initial parking position requirements. To address this problem, literature [1][2][3][4][5][6] uses a fitted curve to optimize the path, which solves the path curvature discontinuity problem and makes the curvature of the vehicle at the start and termination points smaller, but increases the requirements for the size of the parking space. In literature [7], the parallel parking path is divided into two segments, and the vehicle's direction angle is pre-adjusted during the forward process, and then reversed into the parking space, and it is proposed to use two-segment quintic polynomial, which represent the forward section and the reverse section, respectively, and the use of quintic polynomial to generate the path curves solves the problem of the initial parking space posture, but its second curve also increases the requirement for the size of the parking space.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Path Planning for Parallel Parking Based on NSGA-II Algorithm

Tong,

Zhang

2024

Advances in Transdisciplinary Engineering

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In order to improve the safety, parking efficiency and smoothness of the parallel parking of driverless cars, for the parking process needs to meet the path curve curvature is small, parking distance is short, the lane occupation is less, through the analysis of the characteristics of the parallel parking working conditions, put forward a NSGA-II algorithm based on the parking starting point of multi-objective optimization method. Firstly, based on the analysis of vehicle kinematics model, the quintic polynomial curve is used to construct the parking path, and the analytical function between the parking start point and various indexes is established; then, the NSGA-II algorithm is used to optimize the constructed multi-objective model, so as to obtain the optimal optional set of the parking start point; finally, the results of the simulation experiments show that: in order to satisfy the various objectives of the synergistic optimization as well as constraints, the proposed method of the design of the parking start point can achieve the parking path curve of less curvature and shorter parking distance.

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Hierarchical Parking Path Planning Based on Optimal Parking Positions

Cited by 5 publications

References 21 publications

Unmanned cart path planning based on improved A* algorithm

Unmanned cart path planning based on improved A* algorithm

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Multi-Objective Path Planning for Parallel Parking Based on NSGA-II Algorithm

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