Integrated systems based on behaviors
ACM SIGART Bulletin
Behavioral synergy without explicit integration
ACM SIGART Bulletin
How to learn an unknown environment. I: the rectilinear case
Journal of the ACM (JACM)
Connectionist Learning in Behaviour-Based Mobile Robots: A Survey
Artificial Intelligence Review
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring artificial intelligence in the new millennium
Biomimetic navigation models and strategies in animats
AI Communications
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Simple target seek based on behavior
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Simple target seek based on behavior
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Behavior based autonomous navigation using passages as landmarks for path definition
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
WSEAS Transactions on Systems and Control
Automatic programming of robots using genetic programming
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning robust plans for mobile robots from a single trial
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A robotic world model framework designed to facilitate human-robot communication
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Journal of Computational Neuroscience
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A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot''s motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. {\it Spreading of activation} computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.