Ackermann UGV with 2D Mapping for Unknown Environments

UGVs have been used to replace humans in high-risk tasks such as explore caverns, minefields or places contaminated with radiation but also, they are used to research path planners in order to replace human intervention to drive a car. UGVs can be controlled remotely from a safety place to avoid injuries, lethal damage and reduce fatal accidents. However, in order to acquire information about vehicle surroundings, it is necessary to use sensors or cameras that provide information to the remote operator in order to make the best decision of the vehicle course. Correspondingly, the information acquired by the sensors can be used to build an environment map, which will be useful for future applications, or to save a register of the explored area. The work proposed develop and build a vehicle with an Ackermann steering that can be controlled remotely by an operator using a portable computer, with the purpose of explore unknown environments using stereo vision cameras and build a map with information about the surroundings.

[1]  Wilbert Geovanny Aguilar Castillo,et al.  Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles , 2017 .

[2]  Pascal Vasseur,et al.  UAV altitude estimation by mixed stereoscopic vision , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Wilbert G. Aguilar,et al.  Math Model of UAV Multi Rotor Prototype with Fixed Wing Aerodynamic Structure for a Flight Simulator , 2017, AVR.

[4]  J. Underwood,et al.  Towards reliable perception for Unmanned Ground Vehicles in challenging conditions , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Santhosh Kumar,et al.  Four Different Modes to Control Unmanned Ground Vehicle for Military Purpose , 2014 .

[6]  Wilbert G. Aguilar,et al.  Developing of a Video-Based Model for UAV Autonomous Navigation , 2017 .

[7]  AnguloCecilio,et al.  Real-Time Model-Based Video Stabilization for Microaerial Vehicles , 2016 .

[8]  Wilbert G. Aguilar,et al.  SVM and RGB-D Sensor Based Gesture Recognition for UAV Control , 2018, AVR.

[9]  H.H.T. Liu,et al.  A cooperative UAV/UGV platform for wildfire detection and fighting , 2008, 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing.

[10]  Wilbert G. Aguilar,et al.  Statistical Abnormal Crowd Behavior Detection and Simulation for Real-Time Applications , 2017, ICIRA.

[11]  Wilbert G. Aguilar,et al.  Robust Motion Estimation Based on Multiple Monocular Camera for Indoor Autonomous Navigation of Micro Aerial Vehicle , 2018, AVR.

[12]  Alexander Ferrein,et al.  Intuitive visual teleoperation for UGVs using free-look augmented reality displays , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Yandong Tang,et al.  V-disparity Based UGV Obstacle Detection in Rough Outdoor Terrain , 2010 .

[14]  Wilbert G. Aguilar,et al.  3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms , 2016 .

[15]  Artur Sagitov,et al.  Modelling a crawler-type UGV for urban search and rescue in Gazebo environment , 2017 .

[16]  Roberto Manduchi,et al.  Terrain perception for DEMO III , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[17]  Ian Scott,et al.  Analysis of Ackermann Steering Geometry , 2006 .

[18]  Lie Guo,et al.  Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle , 2013 .

[19]  Cecilio Angulo,et al.  Real-Time Model-Based Video Stabilization for Microaerial Vehicles , 2015, Neural Processing Letters.