1,572 research outputs found
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a
challenging task. In order to cope with the requirements of a race scenario, we
propose a far-sighted two step, multi-layered graph-based trajectory planner,
capable to run with speeds up to 212~km/h. The planner is designed to generate
an action set of multiple drivable trajectories, allowing an adjacent behavior
planner to pick the most appropriate action for the global state in the scene.
This method serves objectives such as race line tracking, following, stopping,
overtaking and a velocity profile which enables a handling of the vehicle at
the limit of friction. Thereby, it provides a high update rate, a far planning
horizon and solutions to non-convex scenarios. The capabilities of the proposed
method are demonstrated in simulation and on a real race vehicle.Comment: Accepted at The 22nd IEEE International Conference on Intelligent
Transportation Systems, October 27 - 30, 201
Examining the use of B-splines in parking assist systems
The main objective of the presented study and simulations conducted was to investigate the prospect of using B-spline curves for the automatic parking, i.e. self-driving, or intelligent vehicles. We consider the problem of parallel parking for a non-holonomic vehicle with a known maximum path curvature. The relationship between the properties of the path and the geometry of corresponding parking spot is revealed. The unique properties of B-splines are exploited to synthesize a path that is smooth and of continuous curvature. The contributions of this project are in the generations of better, smooth continuous paths. This improves passenger comfort during the parallel parking maneuver and allow vehicles to park in tighter spots by increasing the feasible range of the parking manoeuver
Path Planning Based on Parametric Curves
Parametric curves are extensively used in engineering. The most commonly used parametric curves are, BĂ©zier, B-splines, (NURBSs), and rational BĂ©zier. Each and every one of them has special features, being the main difference between them the complexity of their mathematical definition. While BĂ©zier curves are the simplest ones, B-splines or NURBSs are more complex. In mobile robotics, two main problems have been addressed with parametric curves. The first one is the definition of an initial trajectory for a mobile robot from a start location to a goal. The path has to be a continuous curve, smooth and easy to manipulate, and the properties of the parametric curves meet these requirements. The second one is the modification of the initial trajectory in real time attending to the dynamic properties of the environment. Parametric curves are capable of enhancing the trajectories produced by path planning algorithms adapting them to the kinematic properties of the robot. In order to avoid obstacles, the shape modification of parametric curves is required. In this chapter, an algorithm is proposed for computing an initial BĂ©zier trajectory of a mobile robot and subsequently modifies it in real time in order to avoid obstacles in a dynamic environment
Hybrid PSO-cubic spline for autonomous robots optimal trajectory planning
This paper presents a new version of the Particle Swarm Optimization algorithm where the particles are
replaced by spline functions. The developed algorithm generates smooth motion trajectories with two times
continuously differentiable curvature avoiding obstacles placed in the workspace. It can be used for autonomous
robot path planning or transport problems. The spline based trajectory generation gives us continuous, smooth and optimized path trajectories. Simulation and experimental results demonstrate the effectiveness of the proposed method.info:eu-repo/semantics/publishedVersio
Adaptive Smoothing for Trajectory Reconstruction
Trajectory reconstruction is the process of inferring the path of a moving
object between successive observations. In this paper, we propose a smoothing
spline -- which we name the V-spline -- that incorporates position and velocity
information and a penalty term that controls acceleration. We introduce a
particular adaptive V-spline designed to control the impact of irregularly
sampled observations and noisy velocity measurements. A cross-validation scheme
for estimating the V-spline parameters is given and we detail the performance
of the V-spline on four particularly challenging test datasets. Finally, an
application of the V-spline to vehicle trajectory reconstruction in two
dimensions is given, in which the penalty term is allowed to further depend on
known operational characteristics of the vehicle.Comment: 25 pages, submitte
Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving
This paper presents an approach, using an anticipatory kinodynamic motion planner, for obtaining the best trajectory and velocity profile for autonomous driving in dynamic complex environments, such as driving in urban scenarios. The planner discretizes the road search space and looks for the best vehicle path and velocity profile at each control period of time, assuming that the static and dynamic objects have been detected. The main contributions of the work are in the anticipatory kinodynamic motion planner, in a fast method for obtaining the -splines for path generation, and in a method to compute and select the best velocity profile at each candidate path that fulfills the vehicle kinodynamic constraints, taking into account the passenger comfort. The method has been developed and tested in MATLAB through a set of simulations in different representative scenarios, involving fixed obstacles and moving vehicles. The outcome of the simulations shows that the anticipatory kinodynamic planner performs correctly in diverse dynamic scenarios, maintaining smooth accelerations for passenger comfortPeer ReviewedPostprint (author's final draft
Learning from Experience for Rapid Generation of Local Car Maneuvers
Being able to rapidly respond to the changing scenes and traffic situations
by generating feasible local paths is of pivotal importance for car autonomy.
We propose to train a deep neural network (DNN) to plan feasible and
nearly-optimal paths for kinematically constrained vehicles in small constant
time. Our DNN model is trained using a novel weakly supervised approach and a
gradient-based policy search. On real and simulated scenes and a large set of
local planning problems, we demonstrate that our approach outperforms the
existing planners with respect to the number of successfully completed tasks.
While the path generation time is about 40 ms, the generated paths are smooth
and comparable to those obtained from conventional path planners
Smooth path planning with Pythagorean-hodoghraph spline curves geometric design and motion control
This thesis addresses two significative problems regarding autonomous systems, namely path and trajectory planning. Path planning deals with finding a suitable path from a start to a goal position by exploiting a given representation of the environment. Trajectory planning schemes govern the motion along the path by generating appropriate reference (path) points.
We propose a two-step approach for the construction of planar smooth collision-free navigation paths. Obstacle avoidance techniques that rely on classical data structures are initially considered for the identification of piecewise linear paths that do not intersect with the obstacles of a given scenario.
In the second step of the scheme we rely on spline interpolation algorithms with tension parameters to provide a smooth planar control strategy. In particular, we consider Pythagorean\u2013hodograph (PH) curves, since they provide an exact computation of fundamental geometric quantities. The vertices of the previously produced piecewise linear paths are interpolated by using a G1 or G2 interpolation scheme with tension based on PH splines. In both cases, a strategy based on the asymptotic analysis of the interpolation scheme is developed in order to get an automatic selection of the tension parameters.
To completely describe the motion along the path we present a configurable trajectory planning strategy for the offline definition of time-dependent C2 piece-wise quintic feedrates. When PH spline curves are considered, the corresponding accurate and efficient CNC interpolator algorithms can be exploited
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