An Optimization-based Motion Planning Method for Autonomous Driving Vehicle

Luo, Shaoshuai; Li, Xiaohui; Sun, Zhenping

doi:10.1109/icus50048.2020.9275009

Cited by 8 publications

(4 citation statements)

References 58 publications

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“…The mathematical description of the gradient descent algorithm is as follows [28]: Given a PF P (x, y), where (x, y) denote the coordinates of a cell, the gradient of the PF at point (x, y) is expressed by the vector:…”

Section: Discrete Gradient Descent For Path Generationmentioning

confidence: 99%

Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

2023

IJIES

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This paper presents an innovative approach to global path planning (GPP) that uniquely integrates an enhanced Bidirectional A* search algorithm, gradient descent smoothing, and orientation filter interpolation. These methods are applied within a quadratic approximation-based potential field and global cost map. The key idea is successfully integrating these powerful techniques, ensuring minimal impact on computational time while enhancing path size and smoothness. Rigorous testing in environments like the Amazon web services (AWS) RoboMaker Hospital Gazebo environment and the sections at the control and systems engineering building at Iraq's University of Technology (CSE-UOT) in Iraq shows significant improvement over existing planners like wavefront GPP and robot operating system (ROS) default GPP. The proposed algorithm reduced pathfinding time by approximately 60.3% and path size by 9.0%. These findings highlight the efficiency of the proposed global planner, suggesting it as a robust solution for autonomous robotic navigation in complex architectural scenarios.

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Section: Discrete Gradient Descent For Path Generationmentioning

confidence: 99%

Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

2023

IJIES

View full text Add to dashboard Cite

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“…Gradient descent is a numerical optimization algorithm known for its small storage capacity and high stability. The gradient of the function (the tangent slope of the function at this point) can effectively determine the direction in which the function value decreases the fastest; this iterative process allows the algorithm to approach the optimal solution step by step [22,23]. The gradient descent algorithm can optimize the curvature of the smoother curve and adjust the curvature of the curve to meet the kinematic requirements of vehicle driving.…”

Section: Path-smoothing Optimization Of the B-spline Curve And Gradie...mentioning

confidence: 99%

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

Wu,

Li,

Gao

et al. 2023

Electronics

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Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

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“…The trajectory planning method can directly find a path that meets the requirements but is not very efficient. This paper mainly focuses on smoothing methods combined with typical geometric methods, which can be divided into two parts: geometric [24][25][26][27][28][29][30] and optimisation [31][32][33][34][35][36] methods.…”

Section: Introductionmentioning

confidence: 99%

“…For the two basic requirements of obstacsle avoidance and smoothing, Luo et al 31 used the gradient optimisation method to extend points in the original path further away from obstacles. They also used variableorder Bezier curves to smoothen the filtered path.…”

Section: Introductionmentioning

confidence: 99%

A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

Cao

Zhang

et al. 2023

Measurement and Control

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Presently, mobile robots experience problems of time consumption, poor security, and high computational complexity in global path-smoothing algorithms. This study presents a multiobjective path-smoothing algorithm, including a path point adjustment algorithm and a turn-smoothing method called the point adjustment algorithm and smoothing. First, the proposed path point adjustment algorithm filters and moves the nodes in an original path to decrease the length of the path and increase the angle of the turns in the path. Second, the proposed turn-smoothing method inserts the B-spline curve into the processed path to smooth the discontinuous turns in the path. Subsequently, the positions of the control points are adjusted based on the property of the B-spline to ensure that the smoothed path can avoid obstacles and satisfy the maximum curvature constraint. Simulation results show that the proposed algorithm can quickly calculate the path satisfying the robot dynamic constraints in various environments combined with different global path planning algorithms. Compared with other mainstream path-smoothing algorithms, this algorithm makes a substantial improvement in path length.

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An Optimization-based Motion Planning Method for Autonomous Driving Vehicle

Cited by 8 publications

References 58 publications

Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

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