The Semi-Supervised Support Vector Machine of Path Planning

2013 ◽  
Author(s):  
Cui Bao Xia ◽  
Wu Nan ◽  
Duan Yong
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Support Vector

Path planning using a Multiclass Support Vector Machine

2016 ◽  
Vol 43 ◽  
pp. 498-509 ◽  
Author(s):  
Néstor Morales ◽  
Jonay Toledo ◽  
Leopoldo Acosta
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Support Vector ◽  
Multiclass Support Vector Machine

scholarly journals Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Luís Carlos Santos ◽  
André Silva Aguiar ◽  
Filipe Neves Santos ◽  
António Valente ◽  
Marcelo Petry
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Support Vector ◽  
Topological Map ◽  
Agricultural Robots ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Two Stages ◽  
Computational Resources ◽  
Processing Techniques

Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot’s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution—called AgRoBPP-bridge—to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.

scholarly journals A Novel Path Planning Method for Articulated Road Roller Using Support Vector Machine and Longest Accessible Path With Course Correction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 182784-182795 ◽  
Author(s):  
Xu Tong ◽  
Chen Siwei ◽  
Wang Dong ◽  
Wu Ti ◽  
Xu Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Planning Method ◽  
Support Vector

A Dynamic Local Path Planning Method for Outdoor Robot Based on Characteristics Extraction of Laser Rangefinder and Extended Support Vector Machine

2016 ◽  
Vol 30 (02) ◽  
pp. 1659004 ◽  
Author(s):  
Lingli Yu ◽  
Kaijun Zhou
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Least Squares Method ◽  
Optimal Path ◽  
Support Vector ◽  
Planning Problem ◽  
Laser Rangefinder ◽  
Local Path Planning ◽  
Unstructured Environment ◽  
Local Path

For dynamic path planning problem under unstructured environment, firstly, successive edge following and least squares method (SEF-LSM) is adopted to extract environment characteristics of laser rangefinder data, and SEF-LSM with logical reasoning (SEF-LSM-LR) is proposed for dynamic obstacles characteristics detection. Furthermore, the perpendicularity (PERP) algorithm is utilized to identify dynamic vehicle, according to the perpendicularity attribute of vehicle. Secondly, all the laser rangefinder scanning points are marked as negative ([Formula: see text]) or positive ([Formula: see text]1), and the scanning points of one dynamic obstacle are marked as the same label. Thirdly, extended support vector machine (ESVM) is designed for outdoor robot local path planning under unstructured environment, which consider the practical start-goal position and heading constraints, robot kinematic constraint, and curvature constraint, moreover, the emergency obstacle is regarded as disturbances during planning processing. Finally, the optimal path is chosen by the shortest distance evaluation function. Lots of outdoor simulations show that the proposed method solve the dynamic planning problem under unstructured environment, and their effectiveness performance are verified for outdoor robot path planning.

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