Although evolutionary algorithms offer an attractive and versatile approach to the automated design of behavior and control structures for mobile robots, they cannot anticipate the detailed structure of specific environments that the robot might have to deal with. Robots must thus possess mechanisms to adapt to the environments they encounter. In particular, mobile robots need stuctures for building and using spatial maps to aid in the successful exploration and navigation of a-priori unknown environments. This paper proposes a biologically inspired computational model for the acquisition and use of spatial memory structures for mobile robot navigation. Preliminary experimental results indicate that the proposed mechanisms can be effectively exploited by evolution in the design of high-performance robots. 1
|
4828
|
Genetic Algorithms
– Goldberg
- 1989
|
|
343
|
A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations
– Kuipers, Byun
- 1991
|
|
263
|
The Hippocampus as a Cognitive Map
– O’KEEFE, NADEL
- 1978
|
|
231
|
Integration of representation into goal-driven behaviour-based robots
– Mataric
- 1992
|
|
210
|
The Organization of Learning
– GALLISTEL
- 1990
|
|
118
|
The hippocampus as a spatial map, preliminary evidence from unit activity in the freely moving rat
– O'Keefe, Dostrovsky
- 1971
|
|
75
|
Dynamics of the hippocampal ensemble code for space. Science 261
– Wilson, McNaughton
- 1993
|
|
70
|
Sensors for Mobile Robots: Theory and Application
– Everett
- 1995
|
|
67
|
Headdirection cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations
– TAUBE, MULLER, et al.
- 1990
|
|
58
|
Spatial learning for navigation in dynamic environments
– Yamauchi, Beer
- 1996
|
|
53
|
A model of hippocampal function
– Burgess, Recce, et al.
- 1994
|
|
49
|
Visual map making for a mobile robot
– Brooks
- 1985
|
|
47
|
editors. Robot Learning
– Connel, Mahadevan
- 1993
|
|
40
|
Passive Map Learning and Visual Place Recognition
– Engelson
- 1994
|
|
39
|
Cognitive maps for mobile robots: A representation for mapping and navigation
– Kortenkamp
- 1993
|
|
34
|
Hebb-Marr networks and the neurobiological representation of action in space
– McNaughton, Nadel
- 1990
|
|
29
|
Learning to control an autonomous robot by distributed genetic algorithms
– Colombetti, Dorigo
- 1992
|
|
27
|
Coping with uncertainty in map learning
– Basye, Dean, et al.
- 1989
|
|
26
|
Real robots, real learning problems
– Brooks, Mataric
- 1993
|
|
25
|
Vector encoding and the vestibular foundations of spatial cognition: Neurophysiological and computational mechanisms
– McNaughton, Knierim, et al.
- 1995
|
|
17
|
Navigating with landmarks: computing goal locations from place codes
– Redish, Touretzky
- 1996
|
|
16
|
On sensor evolution in robotics
– Balakrishnan, Honavar
- 1996
|
|
14
|
Spatial representation in the rat: Conceptual, behavioral, and neurophysiological perspectives
– Leonard, McNaughton
- 1990
|
|
11
|
Model-Based Reinforcement Learning with an Approximate Learned Model
– Kuvayev
- 1997
|
|
9
|
Analysis of neurocontrollers designed by simulated evolution
– Balakrishnan, Honavar
- 1996
|
|
8
|
Some experiments in the evolutionary synthesis of robotic neurocontrollers
– Balakrishnan, Honavar
- 1996
|
|
3
|
Sensors for Mobile Robots: Theory and Application. AKPeters
– Everett
- 1995
|
|
2
|
Animal Behavior. Longman Scientific and Technical
– McFarland
- 1993
|
|
2
|
Problems of Animal Behavior. Longman Scienti c and Technical
– McFarland
- 1989
|
|
1
|
A neurobiologically inspired model of spatial navigation in robots
– Balakrishnan, Honavar
- 1996
|
|
