Algorithm of Obstacle Avoidance for Autonomous Surface Vehicles based on LIDAR Detection

This paper mainly introduces the algorithm of avoiding obstacles based on the detection of LIDAR and gives the results of simulation under environment of MATLAB. The image acquisition system based on laser radar can provide real-time location information to meet the requirements of image acquisition and processing of system. The VFH+ algorithm is used to update angle and speed of the ASV. A method of rapid expansion, corrosion processing of binary image and device were used for the process of laser image processing. Binary image has features of simple, less-space et al., so efficiency of image processing is improved by dealing with multiple pixels at one time. The innovative point of the laser image processing technology is combined with the classical algorithm VFH+ of avoiding obstacle to realize the autonomous navigation system of ASV. The proposed ASV algorithm of avoiding obstacle based on LIDAR detection is applied to the ASV, which has great value of research and application. For example, it was used for ASV with wind and water complementary, water quality monitoring and sampling, and cleaning up the surface of oil pollution.

[1]  Abhijit Mahalanobis,et al.  Performance of multidimensional algorithms for target detection in LADAR imagery , 2002, SPIE Optics + Photonics.

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[4]  Yoram Koren,et al.  Real-time obstacle avoidance for fact mobile robots , 1989, IEEE Trans. Syst. Man Cybern..

[5]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[6]  Kenji Suzuki,et al.  Linear-time connected-component labeling based on sequential local operations , 2003, Comput. Vis. Image Underst..

[7]  Meng Wang,et al.  Fuzzy logic based robot path planning in unknown environment , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Fredrik Gustafsson,et al.  Least Squares Fitting Articulated Objects , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[9]  Fredrik Gustafsson,et al.  Ground Target Recognition Using Rectangle Estimation , 2006, IEEE Transactions on Image Processing.

[10]  Joerg Neulist,et al.  Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images , 2005, SPIE Defense + Commercial Sensing.

[11]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[12]  Asa Persson,et al.  Methods for recognition of natural and man-made objects using laser radar data , 2004, SPIE Defense + Commercial Sensing.

[13]  Koren,et al.  Real-Time Obstacle Avoidance for Fast Mobile Robots , 2022 .