The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle's Navigation

Avoiding collisions with other objects is one of the most basic safety tasks undertaken in the operation of floating vehicles. Addressing this challenge is essential, especially during unmanned vehicle navigation processes in autonomous missions. This paper provides an empirical analysis of the surface target detection possibilities in a water environment, which can be used for the future development of tracking and anti-collision systems for autonomous surface vehicles (ASV). The research focuses on identifying the detection ranges and the field of view for various surface targets. Typical objects that could be met in the water environment were analyzed, including a boat and floating objects. This study describes the challenges of implementing automotive radar sensors for anti-collision tasks in a water environment from the perspective of target detection with the application for small ASV performing tasks on the lake.

[1]  Maged Mikhail,et al.  Navigation software system development for a mobile robot to avoid obstacles in a dynamic environment using laser sensor , 2017, SoutheastCon 2017.

[2]  Karthik Ramasubramanian,et al.  Moving from Legacy 24 GHz to State-of-the-Art 77-GHz Radar , 2018, ATZelektronik worldwide.

[3]  W. Kazimierski,et al.  Verification of multiple model neural tracking filter with ship's radar , 2012, 2012 13th International Radar Symposium.

[4]  Antonios Tsourdos,et al.  Development of Collision Avoidance Algorithms for the C-Enduro USV , 2014 .

[5]  Martin Schneider,et al.  Automotive Radar – Status and Trends , 2005 .

[6]  Andrzej Stateczny,et al.  Selection of GRNN network parameters for the needs of state vector estimation of maneuvering target in ARPA devices , 2006, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[7]  Jose Miguel Almeida,et al.  Radar based collision detection developments on USV ROAZ II , 2009, OCEANS 2009-EUROPE.

[8]  Sanjay Sharma,et al.  Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles , 2017, Journal of Navigation.

[9]  Yuanchang Liu,et al.  Smoothed A* algorithm for practical unmanned surface vehicle path planning , 2019, Applied Ocean Research.

[10]  Tomasz Praczyk,et al.  Neural anti-collision system for Autonomous Surface Vehicle , 2015, Neurocomputing.

[11]  Joohyun Woo,et al.  Obstacle avoidance and target search of an Autonomous Surface Vehicle for 2016 Maritime RobotX challenge , 2017, 2017 IEEE Underwater Technology (UT).

[12]  Chan Gook Park,et al.  Robust performance of Terrain Referenced Navigation using flash LiDAR , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[13]  Ho-Jin Choi,et al.  Neural network-based autonomous navigation for a homecare mobile robot , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[14]  Sven Behnke,et al.  Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner , 2017, Robotics Auton. Syst..

[15]  Ryan Close,et al.  Fusion of lidar and radar for detection of partially obscured objects , 2015, Defense + Security Symposium.

[16]  Alberto Tosi,et al.  Automotive Three-Dimensional Vision Through a Single-Photon Counting SPAD Camera , 2016, IEEE Transactions on Intelligent Transportation Systems.

[17]  Pan Wei,et al.  LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System , 2018, Electronics.

[18]  Branko Ristic,et al.  Feature-based robot navigation using a Doppler-azimuth radar , 2017, Int. J. Control.

[19]  Karthik Ramasubramanian.  Using a complex-baseband architecture in FMCW radar systems , 2017 .

[20]  Alan Wee-Chung Liew,et al.  A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors , 2015, RiTA.

[21]  Siyuan Chen,et al.  Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor , 2017, Sensors.

[22]  Bj⊘rn-Olav H. Eriksen,et al.  Radar-based maritime collision avoidance using dynamic window , 2018, 2018 IEEE Aerospace Conference.

[23]  Hermann Rohling,et al.  New Automotive Applications for Smart Radar Systems , 2002 .

[24]  Hong Bao,et al.  Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS , 2017, Multimedia Tools and Applications.

[25]  I. Gresham,et al.  A 76-77 GHz pulsed-Doppler radar module for autonomous cruise control applications , 2000, 2000 IEEE MTT-S International Microwave Symposium Digest (Cat. No.00CH37017).

[26]  Cheng Kai,et al.  A Research on Intelligent Obstacle Avoidance for Unmanned Surface Vehicles , 2018, 2018 Chinese Automation Congress (CAC).

[27]  Mohd Rizal Arshad,et al.  COLREGs based navigation of riverine Autonomous Surface Vehicle , 2016, 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS).

[28]  Sebastian Brückner.  Maximum Length Sequences for Radar and Synchronization / von Sebastian David Brückner , 2016 .

[29]  David G. Vass,et al.  Proceedings of the Society of Photo-optical Instrumentation Engineers (SPIE) , 1997 .

[30]  Robert A. MacLachlan,et al.  Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[31]  Zhibiao Jiang,et al.  Off-road obstacle sensing using synthetic aperture radar interferometry , 2017 .

[32]  Marion Jaud,et al.  Towards LIDAR-RADAR based terrain mapping , 2015, 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO).

[33]  Eva Volna,et al.  Control of autonomous robot using neural networks , 2017 .

[34]  George M. Williams.  Optimization of eyesafe avalanche photodiode lidar for automobile safety and autonomous navigation systems , 2017 .

[35]  Bo Wang,et al.  Radar-based collision avoidance for unmanned surface vehicles , 2016 .

[36]  Moharam Habibnejad Korayem,et al.  Path planning algorithm in wheeled mobile manipulators based on motion of arms , 2015 .