A Planning Map for Mobile Robots: Speed Control and Paths Finding in a Changing Environment

Authors:
Mathias Quoy;Philippe Gaussier;Sacha Leprêtre;Arnaud Revel;Jean-Paul Banquet
Affiliations:
-;-;-;-;-
Venue:
EWLR-8 Proceedings of the 8th European Workshop on Learning Robots: Advances in Robot Learning
Year:
1999

Citing 10
Cited 0

Visual learning, adaptive expectations, and behavioral conditioning of the mobile robot MAVIN

Neural Networks
A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments

Machine Learning
Categorization, representations, and the dynamics of system-environment interaction: a case study in autonomous systems

Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Place learning and the dynamics of spatial navigation: a neural network approach

Adaptive Behavior
1994 Special Issue: A model of hippocampal function

Neural Networks - Special issue: models of neurodynamics and behavior
1994 Special Issue: Mobile robot visual mapping and localization: A view-based neurocomputational architecture that emulates hippocampal place learning

Neural Networks - Special issue: models of neurodynamics and behavior
A topological neural map for on-line learning: emergence of obstacle avoidance in a mobile robot

SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
The dynamics of recurrent behavior networks

Adaptive Behavior
Planification versus sensory-motor conditioning: what are the issues?

Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Learning View Graphs for Robot Navigation

Autonomous Robots - Special issue on autonomous agents

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present here a neural model for mobile robot action selection and trajectories planning. It is based on the elaboration of a "cognitive map". This cognitive map builds up a graph linking together reachable places. We first demonstrate that this map may be used for the control of the robot speed assuring a convergence to the goal. We show afterwards that this model enables to select between different goals in a static environment and finally in a changing environment.