An obstacle avoidance method for a redundant manipulator controlled through a recurrent neural network

In this paper we study the problem of obstacle avoidance for a redundant manipulator. The manipulator is controlled through an already developed recurrent neural network, called MMC-model (Mean of Multiple Computation), able to solve the kinematics of manipulators in any configuration. This approach solves both problems of direct-and inverse kinematics by simple numerical iterations. The MMC-model here proposed is constituted by a linear part that performs the topological analysis without any constraint and by a second layer, with non-linear blocks used to add the constraints related to both the mechanical structure of the manipulator and the obstacles located in the operative space. The control architecture was evaluated in simulation for a planar manipulator with three links. The robot starting from a given initial configuration is able to reach a target position chosen in the operative space avoiding collisions with an obstacle placed in the plane. The obstacle is identified by simulated sensors placed on each link, they can measure the distance between link and obstacle. The reaction to the obstacle proximity can be modulated through a damping factor that improves the smoothing of the robot trajectory. The good results obtained open the way to a hardware implementation for the real-time control of a redundant manipulator.