# Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence  
[PERSON]  
1 Department of Computer Science, CONACYT-INAOE (Instituto Nacional de Astrofisica,  
Optica y Electronica), Santa Maria Tonanzantia, Puebla 72840, Mexico; [EMAIL_ADDRESS]  
2 The CIDIT (Center for Research, Development and Innovation Tecnolo), Universidad de Ciencia y Tecnologia Descartes, CIDIT-Posgrado, Tuxtla Gutierrez 29065, Mexico  
3 Faculty of Civil Engineering, CONACYT-Universidad Michoacana de San Nicolas de Hidalgo,  
Morelia 58000, Mexico; [EMAIL_ADDRESS]  
4 CONACYT-BUAP, Physical-Mathematical Science Department, Puebla 72570, Mexico;  
and so on. Depending on the environment in which the mobile robot will be implemented, two approaches can be identified [2]; the first one uses the global knowledge of the environment, this means that, at all times, the robot has information about its location, movements, obstacles, and the goal. The second approach uses local information retrieved by range sensors such as sonar, laser, infrared, ultrasonic sensors, video cameras, or Kinect. For example, ultrasonic proximity sensors use a transducer to send and receive high-frequency sound signals. If a target enters the beam, then the sound is sent back to the sensor, causing the output circuit to turn on or off. One advantage [3] of these sensors is that they perform better at detecting objects that are more than one meter away. Light does not affect their operation as it does with other proximity devices. They have high precision detecting objects inside 5 mm. In addition, they can measure the distance through liquids and transparent objects. The pulse duration is proportional to the transducer distance and the object that reflects the closest sound [4]. Applications have been proposed with ultrasonic sensors; for example, the authors in [5] developed an array of ultrasonic sensors with sixteen pieces mounted on a mobile robot to perform real-time 2D and 3D mapping. Another sensor widely used in obstacle avoidance applications is the Microsoft Kinect sensor, which has become one of the most popular depth sensors for research purposes [6], created to recognize the human gesture in a game console and released in November 2010. The Kinect sensors use two devices to generate depth images: (1) An RGB Camera and (2) a 3D depth Sensor. The first one is the USB video camera (\(640\times 480\)) that performs facial recognition, object detection, and tracking; the second one is a 3D Depth Sensor formed by two devices: (a) an infrared laser projector and (b) a monochrome CMOS camera pair. The infrared laser projector (transmitter) incorporates an illumination light unit that projects a structured pattern light onto the objective scene and infers depth from that pattern's deformation. A monochrome CMOS camera (receiver) takes this image from a different point and triangulates to obtain depth information, calculated at each RGB camera pixel. Developing motion models is necessary, along with sensors and navigation. Models are developed based on robot kinematics and locomotion, which means moving an autonomous robot between two places. The kinematic model describes the relationship among inputs, parameters, and system behavior, describing the velocities through second-order differential equations. Different works proposing models for wheeled mobile robots appear in [7,8]. Authors in [9] presented a kinematic modeling scheme to analyze the skid-steered mobile robot. Their results show that the kinematic modeling proposed is useful in a skid-steered robot and tracked vehicles. In [10], a dynamic model for omnidirectional wheeled mobile robots is presented. According to the results, this model considers slip between wheels and motion surface. The friction model response is improved by considering the rigid material's discontinuities among the omnidirectional wheel rollers. On the other hand, mobile robot models are based on different characteristics, such as structure and design.