ANN Method for Control of Robots to Avoid Obstacles


  • Emilia Ciupan Technical University of Cluj-Napoca, Departament of Management and Economical Engineering
  • Florin Lungu Technical University of Cluj-Napoca, Departament of Management and Economical Engineering
  • Cornel Ciupan Technical University of Cluj-Napoca, Departament of Design Engineering and Robotics


Artificial Neural Network (ANN), control, robot, obstacle avoidance


The avoidance of obstacles placed in the workspace of the robot is a
problem which makes controlling them more difficult. The known avoidance methods
used for the robots control are based on bypass trajectory programming or on using
the sensors that detect the position of the obstacle. This paper describes a method of
training industrial robots in order for them to avoid certain obstacles in the workspace.
The method is based on the modelling of the robot’s kinematics by means of an
artificial neural network and by including the neural model in the robot’s controller.
The neural model simulates the robot’s inverse kinematics, and provides the joint
coordinates, as referential values for the controller. The novelty of the method consists
in the deliberately erroneous training of the network, so that, when programming a
direct trajectory in the workspace, the robot avoids a known obstacle.


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