scholarly journals Practical Global and Local Path Planning Algorithm for Autonomous Vehicles Parking

2014 ◽  
Vol 80 (3) ◽  
pp. 308-315
Author(s):  
Quoc Huy DO ◽  
Seiichi MITA ◽  
Keisuke YONEDA
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Global And Local

A smooth local path planning algorithm based on modified visibility graph

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740091 ◽  
Author(s):  
Taizhi Lv ◽  
Maoyan Feng
Keyword(s):  
Path Planning ◽  
Simulation Experiment ◽  
Visibility Graph ◽  
Local Path Planning ◽  
Spline Curves ◽  
Smooth Path ◽  
Planning Algorithm ◽  
Path Search ◽  
Local Path ◽  
Path Planning Algorithm

Path planning is an essential and inevitable problem in robotics. Trapping in local minima and discontinuities often exist in local path planning. To overcome these drawbacks, this paper presents a smooth path planning algorithm based on modified visibility graph. This algorithm consists of three steps: (1) polygons are generated from detected obstacles; (2) a collision-free path is found by simultaneous visibility graph construction and path search by A[Formula: see text] (SVGA); (3) the path is smoothed by B-spline curves and particle swarm optimization (PSO). Simulation experiment results show the effectiveness of this algorithm, and a smooth path can be found fleetly.

Dynamic Path Planning for Robot Navigation Using Sonor Mapping and Neural Networks

1997 ◽  
Vol 119 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Won Soo Yun ◽  
Dong Woo Cho ◽  
Yoon Su Baek
Keyword(s):  
Neural Networks ◽  
Path Planning ◽  
Mobile Robot ◽  
Local Planning ◽  
Sensor Model ◽  
Planning Algorithm ◽  
Local Path ◽  
Time Varying Environment ◽  
Path Planning Algorithm ◽  
Global And Local

This paper presents a new path planning algorithm for safe navigation of a mobile robot in dynamic as well as static environments. The certainty grid concept is adopted to represent the robot’s surroundings and a simple sensor model is developed for fast acquisition of environmental information. The proposed system integrates global and local path planning and has been implemented in a partially known structured environment without loss of generality for an indoor mobile robot. The global planner finds the initial path based on Dijkstra’s algorithm, while the local planning scheme uses three neural networks to follow the initial global path and avoid colliding with static and moving obstacles. Effectiveness of these algorithms is illustrated through simulation and experiment using a real robot. The results show that the proposed algorithm can be efficiently implemented in a time varying environment.

Research on local path planning based on improved RRT algorithm

2021 ◽  
pp. 095440702199362
Author(s):  
Changfu Zong ◽  
Xiaojian Han ◽  
Dong Zhang ◽  
Yang Liu ◽  
Weiqiang Zhao ◽  
...  
Keyword(s):  
Path Planning ◽  
Vehicle Dynamics ◽  
Simulation Software ◽  
Tracking Algorithm ◽  
Expansion Phase ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Actual Size

In order to solve the local path planning of self-driving car in the structured road environment, an improved path planning algorithm named Regional-Sampling RRT (RS-RRT) algorithm was proposed for obstacle avoidance conditions. Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency in the sampling phase. In the expansion phase, considering the actual size of the vehicle and obstacles, combined with the goal of safety and comfort, the separating axis theorem (SAT) method and vehicle dynamics were used to detect the collision among vehicle and surrounding obstacles in real time. In the post-processing stage, the driver’s driving consensus and path smoothing algorithm were combined to correct the planning path. In order to track the generated path, the MPC tracking algorithm was designed based on the Four-Wheel-Independent Electric Vehicle (FWIEV) model. The co-simulation software platform of CarSim and MATLAB/Simulink was employed to verify the effectiveness and feasibility of the path planning and tracking algorithm. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the FWIEV dynamics and path tracking requirements.

scholarly journals Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6642
Author(s):  
Rafal Szczepanski ◽  
Artur Bereit ◽  
Tomasz Tarczewski
Keyword(s):  
Augmented Reality ◽  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Local Minimum ◽  
Artificial Potential Field ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The Artificial Potential Field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of Artificial Potential Field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel Artificial Potential Field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

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