Abstract
This article presents a learning model that simulates the control of an anthropomorphic arm kinematics motion. The objective is to reach and grasp a static prototypic object placed behind different kinds of obstacle in size and position. 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). Our simulation was based on the notion of Via Point, which postulates that the motion planning that is divided into specific successive position of the arm. In order to determine these special points, an experimental protocol has been built and pertinent parameters have been integrated to the model. According to these studies, we propose an original method that takes into account the previous learning modules to determine the entire trajectory of the wrist in order to reach the same object placed behind two successive obstacles. The aim of this approach is to understand better the impact of experience in a task realisation and show that learning can be performed from previous initiation. Some results (applied to obstacle avoidance task) show the efficiency of the proposed method.
Highlights
Since the past twenty years, motion planning is one of the most important research objectives in the robotics field
We have presented an original approach that is integrating several parameters from experimental data in a generic neural network for path planning tasks
According to the notion of via point and obstacle-related information such as obstacle size and location, the trajectory is determined after learning in order to reach and grasp a static prototypic object placed behind an obstacle of varying position and size
Summary
Since the past twenty years, motion planning is one of the most important research objectives in the robotics field. The learning of the reach-and-grasp of an object by a robot is difficult when an obstacle is placed between them. The first one called “local”, which uses only incomplete information of the environment, and is usually implemented in an “on-line” algorithm. In this case, the robot checks up potential collisions during the robot motion and activates a matched strategy to avoid the obstacle. Several authors worked on path-planning problem with obstacles and proposed a large number of methods, such as potential-field Khatib (1985), Krogh
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