Fast Optimization Based Motion Planning and Path-Tracking Control for Car Parking

Zips, Patrik; Böck, Martin; Kugi, Andreas

doi:10.3182/20130904-3-fr-2041.00042

Cited by 8 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, Ning et al implemented a trajectory planning and tracking control scheme for autonomous obstacle avoidance of wheeled inverted pendulum (WIP) vehicles ( Ning et al, 2020 ). Motion planning and trajectory control has been applied to car parking as well ( Zips, Bck & Kugi, 2013 ), where the authors first find a path by solving a static optimization problem and then use a optimal controller for parking. The reader is referred to Chen, Peng & Grizzle (2017) , Guo et al (2018) , Viana et al (2021) , Wang et al (2020) and Zhang et al (2019) for other examples of such algorithms.…”

Section: Related Workmentioning

confidence: 99%

ACO-Kinematic: a hybrid first off the starting block

Chaudhary

Prasad

Chand

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

The use of robots in carrying out various tasks is popular in many industries. In order to carry out a task, a robot has to move from one location to another using shorter, safer and smoother route. For movement, a robot has to know its destination, its previous location, a plan on the path it should take, a method for moving to the new location and a good understanding of its environment. Ultimately, the movement of the robot depends on motion planning and control algorithm. This paper considers a novel solution to the robot navigation problem by proposing a new hybrid algorithm. The hybrid algorithm is designed by combining the ant colony optimization algorithm and kinematic equations of the robot. The planning phase in the algorithm will find a route to the next step which is collision free and the control phase will move the robot to this new step. Ant colony optimization is used to plan a step for a robot and kinematic equations to control and move the robot to a location. By planning and controlling different steps, the hybrid algorithm will enable a robot to reach its destination. The proposed algorithm will be applied to multiple point-mass robot navigation in a multiple obstacle and line segment cluttered environment. In this paper, we are considering a priori known environments with static obstacles. The proposed motion planning and control algorithm is applied to the tractor-trailer robotic system. The results show a collision and obstacle free navigation to the target. This paper also measures the performance of the proposed algorithm using path length and convergence time, comparing it to a classical motion planning and control algorithm, Lyapunov based control scheme (LbCS). The results show that the proposed algorithm performs significantly better than LbCS including the avoidance of local minima.

show abstract

Section: Related Workmentioning

confidence: 99%

ACO-Kinematic: a hybrid first off the starting block

Chaudhary

Prasad

Chand

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…However, the drawback of random sampling methods is the uncertainty in path quality and planning time. [14] transforms the path planning problem to a local static optimization problem and iteratively constructs the solution with a heuristic to change driving directions. Most of these methods are specially designed for parking scenarios and are difficult to integrate into a general motion planning framework.…”

Section: Related Workmentioning

confidence: 99%

Path planning with orientation-aware space exploration guided heuristic search for autonomous parking and maneuvering

Chen

Rickert

Knoll

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Due to the nonholonomic constraints of the vehicle kinematics, parking and maneuvering a car in a narrow clustered space are among the most challenging driving tasks. This paper introduces an extended version of Space Exploration Guided Heuristic Search (SEHS) method, called Orientation-Aware Space Exploration Guided Heuristic Search (OSEHS), to solve the path planning problems for parking and maneuvering. This method considers the orientation of a vehicle in the space exploration phase to achieve knowledge about driving directions. Such information is exploited later in the heuristic search phase to improve the planning efficiency in maneuvering scenarios. This approach is not bound to the specific domain knowledge about a parking or maneuvering task, but obtains the space dimension and orientation information through a generic exploration procedure. Therefore, it is convenient to integrate the maneuvering ability into a general SEHS motion planning framework. Experiments show that the OSEHS approach produces better results than common random-sampling methods and general heuristic search methods.

show abstract

“…Motion planning, in the second layer of this hierarchy, is a demanding task, for which various techniques have been proposed, such as model predictive control, 6,7 fuzzy logic, 8,9 machine learning methods, 10 optimization methods, [11][12][13] Graph-based methods, [14][15][16][17][18][19][20] random exploring tree methods, [21][22][23][24][25] and geometric methods. [26][27][28][29] Optimization methods define a cost function (functional) and try to minimize it, while being subjected to equations of motion of the vehicle (differential constraints), non-holonomic constraints, obstacle constraints, and the initial and final desired position of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…[26][27][28][29] Optimization methods define a cost function (functional) and try to minimize it, while being subjected to equations of motion of the vehicle (differential constraints), non-holonomic constraints, obstacle constraints, and the initial and final desired position of the vehicle. [11][12][13] Li and Shao use the travel time as the cost function so that minimum-time motions are found. 11 Zips et al use a weighted quadratic norm to minimize the final configuration error and steering input change in each step.…”

Section: Introductionmentioning

confidence: 99%

“…11 Zips et al use a weighted quadratic norm to minimize the final configuration error and steering input change in each step. 12 Optimization methods usually need a relevant initial guess, 30 or efficient heuristic rules 13 to converge to global optimum solutions. There are also weighting coefficients in the cost function which require tuning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fast analytical local path planning method with applications in parking scenarios

Ghajar

Alirezaei

Besselink

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In this paper an analytical and geometry-based path planning method is proposed that provides non-holonomic and collision free paths for parking maneuvers. The proposed method is straightforward in the sense that it does not require any sampling or exploring of the configuration space, or relies on any optimization process. Furthermore, the calculation is fast enough to be used in real-time applications for guiding the vehicle in parking scenarios. For any pair of start-goal configurations, the planner presents an infinite set of solutions. These paths have minimum number of driving direction changes as this can be more important for a driver than the path length. They may have any curvature lower than or equal to the maximum specified curvature. Simulations show the superiority of the method in local path planning over conventional methods such as hybrid A* and optimization based approaches in terms of computation time and path quality attributes such as the number of motion primitives and the number of direction changes. To compose a complete motion planning system, the proposed local planner is applied along with hybrid A* as global planner and a path tracking controller. The performance of the motion planning system is evaluated by simulating a valet parking scenario. MATLAB/Simulink is used to simulate the system, and a comprehensive vehicle model in CARSIM is used to represent the vehicle. Simulation results shows the capability of the proposed motion planner to implement automated valet parking tasks while respecting the physical limitations of the vehicle subsystems and driving comfort requirements.

show abstract

Fast Optimization Based Motion Planning and Path-Tracking Control for Car Parking

Cited by 8 publications

References 12 publications

ACO-Kinematic: a hybrid first off the starting block

ACO-Kinematic: a hybrid first off the starting block

Path planning with orientation-aware space exploration guided heuristic search for autonomous parking and maneuvering

A fast analytical local path planning method with applications in parking scenarios

Contact Info

Product

Resources

About