Landmark-based navigation for a mobile robot
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
A Multiagent Approach to Qualitative Landmark-Based Navigation
Autonomous Robots
Ant Colony Optimization
Bee behaviour in multi-agent systems: a bee foraging algorithm
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Stigmergic landmark routing: a routing algorithm for wireless mobile ad-hoc networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Bee-inspired foraging in an embodied swarm
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In this paper, we describe a nature-inspired optimization algorithm based on bee foraging behavior. This algorithm combines the high performance of bee path-integration navigation with ant-like stigmergic behavior in the form of landmarks. More precisely, each individual landmark can be created at any walkable state in the environment and contains a collection of direction markers with which visiting agents can find their way in an unknown environment. A landmark can either be represented by an agent or any other information distributing object (e.g., a RFID). Essentially, we implement ant recruitment behavior based on pheromone. However, instead of using attracting or repelling pheromone in every state of the environment, we only update directional information at key locations in the environment. The resulting algorithm, which we call Stigmergic Landmark Foraging (SLF), proves to be very efficient in terms of building and adapting solutions.