<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10006142" mdate="2017-01-02 00:00:00"> <author>Reza Hossseynie and Amir Jafari</author> <title>Steering Velocity Bounded Mobile Robots in Environments with Partially Known Obstacles</title> <pages>1832 - 1837</pages> <year>2016</year> <volume>10</volume> <number>10</number> <journal>International Journal of Mechanical and Mechatronics Engineering</journal> <ee>https://publications.waset.org/pdf/10006142</ee> <url>https://publications.waset.org/vol/118</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>This paper presents a method for steering velocity bounded mobile robots in environments with partially known stationary obstacles. The exact location of obstacles is unknown and only a probability distribution associated with the location of the obstacles is known. Kinematic model of a 2wheeled differential drive robot is used as the model of mobile robot. The presented control strategy uses the Artificial Potential Field (APF) method for devising a desired direction of movement for the robot at each instant of time while the Constrained Directions Control (CDC) uses the generated direction to produce the control signals required for steering the robot. The location of each obstacle is considered to be the mean value of the 2D probability distribution and similarly, the magnitude of the electric charge in the APF is set as the trace of covariance matrix of the location probability distribution. The method not only captures the challenges of planning the path (i.e. probabilistic nature of the location of unknown obstacles), but it also addresses the output saturation which is considered to be an important issue from the control perspective. Moreover, velocity of the robot can be controlled during the steering. For example, the velocity of robot can be reduced in close vicinity of obstacles and target to ensure safety. Finally, the control strategy is simulated for different scenarios to show how the method can be put into practice.</abstract> <index>Open Science Index 118, 2016</index> </article>