Path-Planning for Autonomous Parking with Dubins Curves

Autonomous cars can offer numerous services to its users, one is fully autonomous parking. There are different challenges involved in autonomous parking on parking spaces such as efficient path planning under constraints or limited sensing of today’s cars. In this paper, we present a path planning approach for automated car parking in unstructured environments. Our system is able to find paths without imposing additional restrictions on either the environment, the final parking position, the number of direction switches, nor the length of the path. Our method consists of a lattice grid search, which yields kinematically-feasible paths and a subsequent optimization step to obtain a dynamically-desirable solution. The main contribution of this paper is in the construction of edges for the grid search through Dubins Curves. We implemented and throughly tested our system both in simulation and on a real Mercedes-Benz E-Class instrumented for automated driving. The parking solutions are found quickly and allow for an accurate execution so that parking is possible for smalland medium-sized parking lots. We believe that this paper is a viable approach towards fully automated parking.

[1]  Sebastian Thrun,et al.  Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments , 2010, Int. J. Robotics Res..

[2]  Cyrill Stachniss,et al.  Autonomous Parking Using Previous Paths , 2016 .

[3]  Alonzo Kelly,et al.  Constrained Motion Planning in Discrete State Spaces , 2005, FSR.

[4]  Roland Siegwart,et al.  On the design of deformable input- / state-lattice graphs , 2010, 2010 IEEE International Conference on Robotics and Automation.

[5]  J. C. Leger.  Menger curvature and rectifiability , 1999 .

[6]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[7]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[8]  L. Shepp,et al.  OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS , 1990 .

[9]  Oliver Brock,et al.  Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles , 2009 .

[10]  Wolfram Burgard,et al.  Autonomous driving in a multi-level parking structure , 2009, 2009 IEEE International Conference on Robotics and Automation.

[11]  Gyu-Sang Cho,et al.  An Efficient Method to Find a Shortest Path for a Car-Like Robot , 2006 .

[12]  Vladimir J. Lumelsky,et al.  Classification of the Dubins set , 2001, Robotics Auton. Syst..

[13]  Dieter Kraft,et al.  Algorithm 733: TOMP–Fortran modules for optimal control calculations , 1994, TOMS.

[14]  Ross A. Knepper,et al.  High Performance State Lattice Planning Using Heuristic Look-Up Tables , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Jürgen Dickmann,et al.  Semi-Markov Process Based Localization Using Radar in Dynamic Environments , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[16]  Saïd Mammar,et al.  Geometric continuous-curvature path planning for automatic parallel parking , 2013, 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC).

[17]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.

[18]  J. Schroder,et al.  Navigating car-like robots in unstructured environments using an obstacle sensitive cost function , 2008, 2008 IEEE Intelligent Vehicles Symposium.