Adding a discrete 'communicate' action to a decentralized multi-robot exploration policy, with a reward that weights information gained by sharing, reduces exploration steps and overlap in simulated environments.
Robotic Exploration for Mapping
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abstract
Robotic Exploration has evolved rapidly in the past two decades as new and more complex techniques have been created to explore unknown regions efficiently. Exciting advancements in exploration, autonomous navigation, and sensor technology have created opportunities for robots to be utilized in new environments and for new objectives ranging from mapping of abandon mines and deep oceans to the efficient creation of indoor models for navigation and search. In this paper we present and discuss a number of examples in research literature of these recent advancements, specifically focusing on robotic exploration algorithms for unmanned vehicles.
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Investigating the Impact of Communication-Induced Action Space on Exploration of Unknown Environments with Decentralized Multi-Agent Reinforcement Learning
Adding a discrete 'communicate' action to a decentralized multi-robot exploration policy, with a reward that weights information gained by sharing, reduces exploration steps and overlap in simulated environments.