Motion Planning Of Autonomous Vehicles Research Articles

Minimizing the maximum curvature of quadratic Bézier curves with a tetragonal concave polygonal boundary constraint

In curve design such as highway design and motion planning of autonomous vehicles, it may be important to minimize the maximum curvature. In this paper we address the problem of minimizing the maximum curvature of a quadratic Bézier curve within a boundary constraint determined by a tetragonal concave polygon. The curve is parameterized by lengths between its control points, called the “control lengths”. Finally, numerical results demonstrate applicability of the method to smooth a piecewise linear path resulting from a path search technique. The results apply whenever it is desired to have a smooth transition between intersecting straight lines.

Optimal Dynamic Motion Planning of Autonomous Vehicles by a Structured Genetic Algorithm

This paper presents a technic for searching and analyzing the time optimal path and motion of autonomous vehicles on the three dimensional terrains by using a structured genetic algorithm. The terrain is represented by a continues B-spline patch, and the path is obtained by B-spline curve on the patch. The time optimal motions are computed considering with vehicle dynamics and several physical constraints along the paths. The control points of the optimal path presenting B-spline curve, which are the optimized parameters, are coded to chromosomes structurally. We show a data processing system of searching the time optimal path for autonomous vehicles.

Dynamic motion planning of autonomous vehicles

A method for planning the motions of autonomous vehicles moving on general terrains is presented that obtains the geometric path and vehicle speeds that minimize motion time considering vehicle dynamics, terrain topography, obstacles, and surface mobility. The terrain is represented by a smooth cubic B patch, and the geometric path consists of a B spline curve mapped to the surface. The time-optimal motions are computed by first obtaining the best obstacle-free path from all paths represented by a uniform grid. This path is further optimized using a local optimization procedure, using the optimal motion time along the path as the cost function and the control points of a B spline as the optimizing parameters. Examples are presented that demonstrate the method for a simple dynamic model of a vehicle moving on mountainous terrain.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>