&lt;title&gt;GORP: a new method for mobile robot path-planning problem&lt;/title&gt;

Noorhosseini, S. M.; Malowany, A.S.

doi:10.1117/12.143811

Cited by 5 publications

(1 citation statement)

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“…Several authors have worked on this path planning problem involving obstacles and propose a large number of methods, such as potentialfield, [4][5][6] or the wall-following method 7 that continues to follow the obstacle's contours until it has passed by the obstacle, and the goal-oriented recursive path planning method. 8 Koren and all used the APF method and assumed that each object in the environment exerts a repulsive force on the mobile robot whereas the goal exerts an attractive force on it. The resulting force is then computed at each step and used to determine the direction of movement for the next step.…”

Section: Introductionmentioning

confidence: 99%

A neural network architecture to learn arm motion planning in grasping tasks with obstacle avoidance

Bendahan

Gorce

2005

Robotica

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In this article, we present a learning model that can control the kinematics motion of a simulated anthropomorphic arm in reaching and grasping tasks of a static prototypic object placed behind an obstacle of varying position and size. The network, composed of two generic neural network modules, learns to combine multi-modal arm-related information (trajectory parameters) as well as obstacle-related information (obstacle size and location). We based our simulation on the Via Point notion, which postulates that the reach motion planning is divided into successive positions of the arm. In order to determine these particular positions, some specific parameters have been extracted from an experimental protocol and constitute the pertinent parameters to be integrated into the model. This net of neural net determines the total path able to reach and grasp the prototypic object while avoiding an obstacle.

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