Adaptive Sampling for Multi-Robot Wide-Area Exploration

— The exploration problem is a central issue in mobile robotics. A complete coverage is not practical if the environment is large with a few small hotspots, and the sampling cost is high. So, it is desirable to build robot teams that can coordinate to maximize sampling at these hotspots while minimizing resource costs, and consequently learn more accurately about properties of such environmental phenomena. An important issue in designing such teams is the exploration strategy. The contribution of this paper is in the evaluation of an adaptive exploration strategy called Adaptive Cluster Sampling (ACS), which is demonstrated to reduce the resource costs (i.e., mission time and energy consumption) of a robot team, and yield more information about the environment by directing robot exploration towards hotspots. Due to the adaptive nature of the strategy, it is not obvious how the sampled data can be used to provide unbiased, low-variance estimates of the properties. This paper therefore...

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Kian Hsiang Low, Geoffrey J. Gordon, John M. Dolan, Pradeep K. Khosla
• Keywords: Exploration Strategy, ICRA 2007, Resource Costs, Robot Team, Robotics