scholarly journals Path Planning for Autonomous Vehicles Using QZSS and Satellite Visibility Map

2013 ◽  
Vol 25 (2) ◽  
pp. 400-407 ◽  
Author(s):  
Mitsunori Kitamura ◽  
◽  
Yoichi Yasuoka ◽  
Taro Suzuki ◽  
Yoshiharu Amano ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Dead Reckoning ◽  
Path Following ◽  
Planning Method ◽  
Self Control ◽  
Potential Fields ◽  
Visibility Map ◽  
Gps Satellites ◽  
Satellite Visibility

This paper describes a path planning method that uses the Quasi-Zenith Satellites System(QZSS) and a satellite visibility map for autonomous vehicles. QZSS is a positioning system operated by Japan that has an effect similar to an increase in the number of GPS satellites. Therefore, QZSS can be used to improve the availability of GPS positioning. A satellite visibility map is a special map that simulates the number of visible satellites at all points on the map. The vehicle can use the satellite visibility map to determine the points that receive more satellite signals. The proposed method generates the artificial potential fields from the satellite visibility map and obstacle information around the vehicle, and it generates the path following the potential fields. Thereby, the vehicle can select the path that has more satellite signals, improving the availability of GPS fixed solutions. Hence, the vehicle can reduce the accumulated error by dead reckoning, and it can improve the safety of self-control. In this study, we evaluate the satellite visibility maps and the path planning method. The results show that the proposed method does improve the availability of GPS fixed solutions.

scholarly journals A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090320
Author(s):  
Peng Li ◽  
Cai-yun Yang ◽  
Rui Wang ◽  
Shuo Wang
Keyword(s):  
Path Planning ◽  
Information Entropy ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Coverage Area ◽  
High Coverage ◽  
Full Coverage ◽  
Localization And Mapping

The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

scholarly journals Path planning with fractional potential fields for autonomous vehicles

2017 ◽  
Vol 50 (1) ◽  
pp. 14533-14538 ◽  
Author(s):  
Julien Moreau ◽  
Pierre Melchior ◽  
Stéphane Victor ◽  
François Aioun ◽  
Franck Guillemard
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Potential Fields

New interpretation of fractional potential fields for robust path planning

2019 ◽  
Vol 22 (1) ◽  
pp. 113-127 ◽  
Author(s):  
Jean-Baptiste Receveur ◽  
Stéphane Victor ◽  
Pierre Melchior
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Methodological Approach ◽  
Potential Fields ◽  
Attractive Potential ◽  
On Line ◽  
New Interpretation ◽  
Definition Of ◽  
Intermediate Targets

Abstract Trajectory planning for autonomous vehicles is a research topical subject. In previous studies, optimal intermediate targets have been used in the Potential Fields (PFs). PFs are only a path planning method, or a reactive obstacle avoidance method and not a trajectory tracking method. In this article, the PFs are interpreted as an on-line control method to follow an optimal trajectory. An analysis and methodological approach to design the attractive potential as a robust controller are proposed, and a new definition of a fractional repulsive potential to characterize the dangerousness of obstacles is developed. Simulation results on autonomous vehicles are given.

A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments

2017 ◽  
Vol 44 (4) ◽  
pp. 406-415 ◽  
Author(s):  
Jiajia Chen ◽  
Wuhua Jiang ◽  
Pan Zhao ◽  
Jinfang Hu
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Planning Method ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Planning Problem ◽  
Fuzzy Support Vector Machine ◽  
Content Type

Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm. Findings The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance. Originality/value This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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