A comparative analysis of artificial immune network models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An immunity approach to strategic behavioral control
Engineering Applications of Artificial Intelligence
An immunological approach to mobile robot reactive navigation
Applied Soft Computing
Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
An Idiotypic Immune Network as a Short-Term Learning Architecture for Mobile Robots
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
INDIE: An Artificial Immune Network for On-Line Density Estimation
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Location-Aware Error Control Scheme of Route Multicast for Moving Agents
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot
Applied Soft Computing
Autonomous Mobile Robot Navigation using Artificial Immune System
Proceedings of Conference on Advances In Robotics
Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment
Intelligent Service Robotics
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This paper investigates an autonomous control system of a mobile robot based on the immune network theory. The immune network navigates the robot to solve a multiobjective task, namely, garbage collection: the robot must find and collect garbage, while it establishes a trajectory without colliding with obstacles, and return to the base before it runs out of energy. Each network node corresponds to a specific antibody and describes a particular control action for the robot. The antigens are the current state of the robot, read from a set of internal and external sensors. The network dynamics corresponds to the variation of antibody concentration levels, which change according to both mutual interaction of antibody nodes and of antibodies and antigens. It is proposed an evolutionary mechanism to determine the network configuration, that is, the parameters that define those interactions. Simulation results suggest that the proposal presented is very promising.