From G<sup>2</sup> to G<sup>3</sup> Continuity: Continuous Curvature Rate Steering Functions for Sampling-Based Nonholonomic Motion Planning

Banzhaf, Holger; Berinpanathan, Nijanthan; Nienhüser, Dennis; Zöllner, J. Marius

doi:10.1109/ivs.2018.8500653

Cited by 15 publications

(33 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [24], the authors introduce continuous curvature rate (CCR), and hybrid curvature rate (HCR) curves. These curves are G3 curves in which the derivative of curvature with respect to arc-length, σ, is a piece-wise linear function of the arclength s, and the second derivative of curvature with respect to arc-length, ρ, is piece-wise constant.…”

Section: B Related Workmentioning

confidence: 99%

Tunable Trajectory Planner Using G3 Curves

Botros¹,

Smith²

2021

Preprint

View full text Add to dashboard Cite

Trajectory planning is commonly used as part of a local planner in autonomous driving. This paper considers the problem of planning a continuous-curvature-rate trajectory between fixed start and goal states that minimizes a tunable trade-off between passenger comfort and travel time. The problem is an instance of infinite dimensional optimization over two continuous functions: a path, and a velocity profile. We propose a simplification of this problem that facilitates the discretization of both functions. This paper also proposes a method to quickly generate minimal-length paths between start and goal states based on a single tuning parameter: the second derivative of curvature. Furthermore, we discretize the set of velocity profiles along a given path into a selection of acceleration way-points along the path. Gradient-descent is then employed to minimize cost over feasible choices of the second derivative of curvature, and acceleration way-points, resulting in a method that repeatedly solves the path and velocity profiles in an iterative fashion. Numerical examples are provided to illustrate the benefits of the proposed methods.

show abstract

Section: B Related Workmentioning

confidence: 99%

Tunable Trajectory Planner Using G3 Curves

Botros¹,

Smith²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…. ; s k ) the d-dimensional signal of the multi-sensor system, the signal variation over time can be linked to the moving vehicle twist: s =L svm (6) with:L s =LT m (7) whereL andT m are obtained by concatenating either diagonally or vertically, respectively, matricesL i and…”

Section: B Multi-sensor Modelingmentioning

confidence: 99%

“…Path planning approaches have been heavily investigated in recent years. Among the different planning techniques it is possible to distinguish between geometric approaches, with either constant turning radius [3], [4] using saturated feedback controllers or continuouscurvature planning using clothoids [5], [6], using continuous curvature rate steering functions based on cubic spirals [7]; heuristic approaches [8] and machine learning techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

David

Kermorgant

Dominguez-Quijada

et al. 2020

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)

View full text Add to dashboard Cite

This paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach in order to have constraint-based backward non-parallel (perpendicular and diagonal) parking maneuvers capable of dealing with moving pedestrians and, if necessary, performing multiple maneuvers. Our technique relies solely in sensor data expressed relative to the vehicle and therefore no localization is inherently required. Since the proposed approach does not plan any path and instead the controller maneuvers the vehicle directly, the classical path planning related issues are avoided. Real experimentation validates the effectiveness of our approach.

show abstract

“…In the literature, several approaches to solve these steps have been suggested. One common approach is to use B-splines or Bezier curves to compute smooth paths for differentially flat systems, either to smoothen a sequence of waypoints, for example generated by a classical path planner [65,85] or as steering functions within a sampling-based motion planner [7,60]. The use of these methods are computationally efficient since the model of the system can be described analytically.…”

Section: Definition 29 (Path Trajectory Relation) a Path Is Represementioning

confidence: 99%

On Motion Planning Using Numerical Optimal Control

Bergman

2019

Linköping Studies in Science and Technology. Licentiate Thesis

View full text Add to dashboard Cite

show abstract

From G² to G³ Continuity: Continuous Curvature Rate Steering Functions for Sampling-Based Nonholonomic Motion Planning

Cited by 15 publications

References 19 publications

Tunable Trajectory Planner Using G3 Curves

Tunable Trajectory Planner Using G3 Curves

Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

On Motion Planning Using Numerical Optimal Control

Contact Info

Product

Resources

About

From G2 to G3 Continuity: Continuous Curvature Rate Steering Functions for Sampling-Based Nonholonomic Motion Planning

Cited by 15 publications

References 19 publications

Tunable Trajectory Planner Using G3 Curves

Tunable Trajectory Planner Using G3 Curves

Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

On Motion Planning Using Numerical Optimal Control

Contact Info

Product

Resources

About

From G² to G³ Continuity: Continuous Curvature Rate Steering Functions for Sampling-Based Nonholonomic Motion Planning