Path Planning Method of Self-Propelled Bridge Inspection Vehicle based on Support Vector Machine

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.

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A case study of using a support vector machine on bridge inspection data

Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges ◽

10.1201/9781315189390-255 ◽

2018 ◽

pp. 1873-1880

Author(s):

Arong ◽

S. Murakami ◽

Yiliguoqi

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Bridge Inspection ◽

Inspection Data

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Analysis of paths by the multi-constraints support vector machine method for path planning

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978368 ◽

2017 ◽

Author(s):

Chen Qingyang

Keyword(s):

Support Vector Machine ◽

Path Planning ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method

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

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-11-2016-0301 ◽

2017 ◽

Vol 44 (4) ◽

pp. 406-415 ◽

Cited By ~ 3

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.

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416590047 ◽

2016 ◽

Vol 30 (02) ◽

pp. 1659004 ◽

Cited By ~ 7

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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