Object Recognition for Obstacles-free Trajectories Applied to Navigation Control

Medina-Melendez, Wilfredis; Fermín, Leonardo; Cappelletto, José; Estévez, P. A.; Fernández-López, Gerardo; Grieco, Juan C.

doi:10.5772/4997

Cited by 1 publication

(1 citation statement)

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“…An artificial vision system detects obstacles in the workspace and provides this information to the system, while the other input to the overall system is the given Target Trajectory Field. Artificial vision systems for similar tasks has been developed [19], whereas the details are out of the scope of this paper, and it could be considered, without loss of generality, that the field generation system already has a map of the robot's workspace. The information provided by both the map of the workspace and the Target Trajectory Field (angle reference) is used to calculate the inputs of the Fuzzy Inference System (FIS), which are: the curl of the target field and an extension of the Voronoi Diagram.…”

Section: Overview Of the Proposed Algorithmmentioning

confidence: 99%

Fuzzy logic based speed planning for autonomous navigation under Velocity Field Control

Pérez-D’Arpino

Medina-Melendez

Guzmán

et al. 2009

2009 IEEE International Conference on Mechatronics

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Velocity fields techniques are often used in the path planning and control problem in autonomous navigation. Velocity fields are compounded by vectors that can be expressed in polar form, i.e. Vx = iVl . cos(n) and Vy = IVI . sin(n). In the proposed technique, the problem is divided into two separate problems: the calculation of the orientation angle n, which was treated in a previous work, and the calculation of the linear velocity IVI. This paper addresses the calculation of IVI given a trajectory vector field that encodes n, thus generating the speed reference for a Velocity Field Controller. The linear velocity I V I is generated through a Mamdani-type Fuzzy Inference System, that considers the curl of the trajectory vector field, and an extension of the Voronoi Diagram. Results obtained in both simulations and tests with a real robot show that an adequate intelligent velocity behavior, and a smooth obstacle avoidance are achieved.

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Section: Overview Of the Proposed Algorithmmentioning

confidence: 99%