Title :
Run-time robot planning
Author :
Sanguineti, V. ; Morasso, P. ; Tsuji, T.
Author_Institution :
Dept. of Inf. Syst. & Telecomm., Genoa Univ., Italy
Abstract :
We (1993) have developed a neural network architecture which learns a forward model of a redundant manipulator (via self-supervised training) as a map of normalized radial basis neurons and inverts the model by means of run-time gradient descent of a task-related potential field. In this paper, we propose a distributed model for the computation of the field, which is consistent with the model-inversion map; and we discuss the problem of self-synchronization between the gradient-descent process and a process for the generation of virtual trajectories of the end-effector.
Keywords :
neural nets; path planning; robots; synchronisation; unsupervised learning; distributed model; model-inversion map; neural network; run-time gradient descent; run-time robot planning; self-supervised training; self-synchronization; task-related potential field; virtual trajectories; Distributed computing; Distributed power generation; Informatics; Power engineering and energy; Power generation; Power system modeling; Power system planning; Prototypes; Robots; Runtime;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714309
