Distributed planning and control of intelligent robot´s arm motion based on symbolic and neural processing

In this work, we present our view of an intelligent robotic manipulator that can work autonomously in a dynamically changing environment. The increased intelligence is needed to enable the manipulator to react to changing environments without contacting a supervising coordinator. This approach requires increased computational capabilities on the part of the manipulator. We show how the computational requirements of such an autonomous manipulator can be satisfied by a combination of neural network-based processing modules. We use symbolic computation methods to construct these neural networks (thereby eliminating training times), and sensors to provide the signals to recognize changes in the environment