TY - CHAP
T1 - Learning visual landmarks for mobile robot topological navigation
AU - Mata, Mario
AU - Armingol, Jose Maria
AU - de la Escalera, Arturo
PY - 2005
Y1 - 2005
N2 - Relevant progress has been done, within the Robotics field, in mechanical systems, actuators, control and planning. This fact, allows a wide application of industrial robots, where manipulator arms, Cartesian robots, etc., widely outcomes human capacity. However, the achievement of a robust and reliable autonomous mobile robot, with ability to evolve and accomplish general tasks in unconstrained environments, is still far from accomplishment. This is due, mainly, because autonomous mobile robots suffer the limitations of nowadays perception systems. A robot has to perceive its environment in order to interact (move, find and manipulate objects, etc.) with it. Perception allows making an internal representation (model) of the environment, which has to be used for moving, avoiding collision, finding its position and its way to the target, and finding objects to manipulate them. Without a sufficient environment perception, the robot simply can’t make any secure displacement or interaction, even with extremely efficient motion or planning systems. The more unstructured an environment is, the most dependent the robot is on its sensorial system. The success of industrial robotics relies on rigidly controlled and planned environments, and a total control over robot’s position in every moment. But as the environment structure degree decreases, robot capacity gets limited.
AB - Relevant progress has been done, within the Robotics field, in mechanical systems, actuators, control and planning. This fact, allows a wide application of industrial robots, where manipulator arms, Cartesian robots, etc., widely outcomes human capacity. However, the achievement of a robust and reliable autonomous mobile robot, with ability to evolve and accomplish general tasks in unconstrained environments, is still far from accomplishment. This is due, mainly, because autonomous mobile robots suffer the limitations of nowadays perception systems. A robot has to perceive its environment in order to interact (move, find and manipulate objects, etc.) with it. Perception allows making an internal representation (model) of the environment, which has to be used for moving, avoiding collision, finding its position and its way to the target, and finding objects to manipulate them. Without a sufficient environment perception, the robot simply can’t make any secure displacement or interaction, even with extremely efficient motion or planning systems. The more unstructured an environment is, the most dependent the robot is on its sensorial system. The success of industrial robotics relies on rigidly controlled and planned environments, and a total control over robot’s position in every moment. But as the environment structure degree decreases, robot capacity gets limited.
KW - mobile robot
KW - training image
KW - deformable model
KW - symbolic information
KW - mobile robot navigation
U2 - 10.1007/11504634_1
DO - 10.1007/11504634_1
M3 - Chapter (peer-reviewed)
SN - 9783540265498
T3 - Studies in Computational Intelligence
SP - 1
EP - 55
BT - Machine Learning and Robot Perception
A2 - Apolloni, Bruno
A2 - Ghosh, Ashish
A2 - Alpaslan, Ferda
A2 - C. Jain, Lakhmi
A2 - Patnaik, Srikanta
PB - Springer Nature
CY - Berlin
ER -