scholarly journals High-Definition Map-based Local Path Planning for Dynamic and Static Obstacle Avoidance

2021 ◽  
Vol 16 (2) ◽  
pp. 112-121
Author(s):  
Euigon Jung ◽  
Wonho Song ◽  
Hyun Myung
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
High Definition ◽  
Local Path Planning ◽  
Local Path

scholarly journals 3D Vision-Based Local Path Planning System of a Humanoid Robot for Obstacle Avoidance

2013 ◽  
Vol 8 (4) ◽  
pp. 879-888 ◽  
Author(s):  
Tae-Koo Kang ◽  
Myo-Taeg Lim ◽  
Gwi-Tae Park ◽  
Dong W. Kim
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Humanoid Robot ◽  
Planning System ◽  
3D Vision ◽  
Local Path Planning ◽  
Local Path

scholarly journals A Hierarchical Control System for Autonomous Driving towards Urban Challenges

2020 ◽  
Vol 10 (10) ◽  
pp. 3543 ◽  
Author(s):  
Nam Dinh Van ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Gon-Woo Kim
Keyword(s):  
Decision Making ◽  
Control System ◽  
Path Planning ◽  
Hierarchical Control ◽  
Longitudinal Direction ◽  
High Definition ◽  
Planning And Control ◽  
Local Path Planning ◽  
Local Path ◽  
And Control

In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario.

scholarly journals Amorphous Obstacle Avoidance Based on APF Methods for Local Path Planning

2011 ◽  
Vol 21 (1) ◽  
pp. 19-24
Author(s):  
Jong-Yeon Lee ◽  
Hah-Min Jung ◽  
Dong-Hun Kim
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Local Path Planning ◽  
Local Path

scholarly journals Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qisong Song ◽  
Shaobo Li ◽  
Jing Yang ◽  
Qiang Bai ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Path Length ◽  
Decision Model ◽  
Ant Colony ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Local Path ◽  
Safe Path

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

Observability-based local path planning and obstacle avoidance using bearing-only measurements

2013 ◽  
Vol 61 (12) ◽  
pp. 1392-1405 ◽  
Author(s):  
Huili Yu ◽  
Rajnikant Sharma ◽  
Randal W. Beard ◽  
Clark N. Taylor
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Local Path Planning ◽  
Local Path

Local Path Planning for Intelligent Vehicle Obstacle Avoidance Based on Dubins Curve and Tentacle Algorithm

2017 ◽  
Author(s):  
Lingfei Wu ◽  
Hongshan Zha ◽  
Caijing Xiu ◽  
Qiaojun He
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Intelligent Vehicle ◽  
Local Path Planning ◽  
Local Path

Online UAS local path-planning algorithm for outdoors obstacle avoidance based on attractive and repulsive potential fields

2016 ◽  
Author(s):  
Gary Elias Dimas Flores ◽  
Eduardo Steed Espinoza Quesada ◽  
Sergio Salazar Cruz ◽  
Luis Rodolfo Garcia Carrillo ◽  
Rogelio Lozano
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Fields ◽  
Repulsive Potential ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm

A feature information based VPH for local path planning with obstacle avoidance of the mobile robot

2007 ◽  
Author(s):  
Tae-Seok Oh ◽  
Yun-Su Shin ◽  
Sung-Yong Yun ◽  
Wang-Heon Lee ◽  
Il-Hwan Kim
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Local Path Planning ◽  
Feature Information ◽  
Local Path
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