Intersection condition-orientated unmanned vehicle trajectory planning method based on Bezier curve and VFH algorithm

Abstract

The present invention provides an intersection condition-orientated unmanned vehicle trajectory planning method based on the Bezier curve and the VFH algorithm. The method includes the following steps that: 1) the starting point pose P0 (x0,y0,theta0) and destination point pose P3 (x3,y3,theta3) of current trajectory planning are acquired; 2) a trajectory cluster A1 from the starting point pose P0 (x0,y0,theta0) to the destination point pose P3 (x3,y3,theta3) is generated through adopting a three-order Bezier curve model; 3) the trajectory cluster A1 is screened according to a maximum curvature constraint, so that a trajectory cluster A2 is obtained, collision detection is performed on A2, so that a collision-free trajectory cluster A3 is obtained; 4) if A3 is not empty, an optimal trajectory is selected from A3 according to a trajectory smoothest principle and is outputted to a control layer, and the method is terminated, otherwise, the method shifts to step 5; 5) a movement region in the original VFH algorithm is improved, so that a fan-shaped movement region is built; 6) obstacle information is utilized to establish a grid map; 7) the fan-shaped movement region is divided into a plurality of fan-shaped regions, and whether an obstacle exists is judged; 8) the Bezier curve is used in combination, and optimal trajectory points are selected; and 9) with a discrete point set generated in the step 8 adopted as control points, a B-spline curve is generated, and the B-spline curve is adopted as the final trajectory of an unmanned vehicle.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation systems, in particular to a real-time trajectory planning method for an unmanned vehicle in a complex traffic scene such as an intersection. Background technique [0002] With the rapid development of the automobile industry and computer technology, driverless vehicles have made unprecedented progress in the field of robotics. The decision-making system is the "brain" of the unmanned vehicle. After the perception system recognizes the surrounding environment, it needs to make safe and executable decisions, determine the current effective behavior and the target state of the behavior, and then plan the trajectory. The intersection is the node of the topological structure of the traffic network, and it is the frequent location of traffic accidents. Therefore, it is of great significance to solve the trajectory planning problem of unmanned vehicles at intersections. The classic t...

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Problems solved by technology

[0009] However, the VFH algorithm is designed for mobile robots, and it often fails to achieve the expected goals when used directly on unmanned vehicles. There are three main problems: the original VFH algorithm is a real-time motion planning method based on perception data, and it The use will inevitably lead to unsmooth motion trajectory and "oscillation" phenomenon [6]; some motion state points in the active area of ​​the original VFH algorithm are inaccessible to unmanned vehicles; the VFH algorithm obtains a sparse set of position points , lack of other motion state information, unable to guide the vehicle [7]

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