Abstract
Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.
Highlights
In recent years, with the rapid development of smart vehicles, the advantages of smart vehicles themselves have become increasingly prominent
Sampling-based search algorithms include probability map algorithms and Rapidly-Exploring Random Tree algorithms. e Rapidly-Exploring Random Tree algorithm is a path-planning algorithm proposed by LaValle [10, 11]
Its advantages include the following four aspects: first, it does not need to model the planning space and is a random sampling algorithm; second, it considers the objective constraints of unmanned vehicles; third, it is suitable to solve the path-planning problem under dynamic and multiobstacle conditions; and fourth, it can be applied to the pathplanning problem under the high-dimensional environment. erefore, it has been widely used [12]. e basic Rapidly-Exploring Random Tree algorithm has the following disadvantages in path planning: first, the path is randomly generated, the path is biased; second, the random tree is nonoriented in the search process; and third, the convergence speed is slow, and the search efficiency is low [13, 14]
Summary
With the rapid development of smart vehicles, the advantages of smart vehicles themselves have become increasingly prominent. E Rapidly-Exploring Random Tree algorithm is a path-planning algorithm proposed by LaValle [10, 11]. E Rapidly-Exploring Random Tree algorithm, which is biased to search and bidirectional expansion, improves the convergence speed and search efficiency but does not overcome the randomness when random trees generate nodes [20, 21]. Song Xiaolin proposed to introduce heuristic function into the Rapidly-Exploring Random Tree algorithm in 2017, which makes the search tree more oriented in the search process, but the algorithm is easy to fall into a dead cycle in path planning [24]. Is paper proposes an improved intelligent vehicle path-planning algorithm based on a Rapidly-Exploring Random Tree, which uses a cyclic alternating iterative search method to generate new nodes. Is paper proposes an improved intelligent vehicle path-planning algorithm based on a Rapidly-Exploring Random Tree, which uses a cyclic alternating iterative search method to generate new nodes. e bidirectional random tree expands simultaneously. e turning angle constraint of the vehicle is increased, the generated nodes are optimized, and the path is smoothened. e shortcomings of the Rapidly-Exploring Random Tree algorithm in path planning are improved
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