As robotic platforms have become more capable and autonomous, they have increasingly been utilized in time sensitive applications such as search and rescue. To that end, we have developed a system for teams of robots to efficiently explore an environment while taking sensor measurements. The system utilizes an information seeking algorithm that generates high priority points of interest based on the highest expected information gained per distance travelled. In order to coordinate multiple robots, the system partitions the area into different regions according to the effort needed to explore each region. Robots are assigned different regions to measure in order to minimize repetition of work and reduce interference between each robot.We present an information rate adaptive sampling approach for tasking robots within an environment to gather sensor measurements. We evaluated our approach within a simulation environment with one to four robots. Multiple robots are coordinated through our region segmentation approach. The data shows efficiency gains through the use of adaptive information gain rate tasking above a naïve closest point approach. We also see positive results from using the region segmentation technique. We further the experimentation by testing the algorithm on real world robots and verify the results in real world experimentation.

Adaptive sampling has been shown to be an effective method for modeling environmental fields, such as algae concentrations in the ocean. In adaptive sampling, a robot adapts its sampling trajectory based on data that it is collecting. This data is often aggregated into models, using techniques such as Gaussian Process (G P) regression. The (hyper-)parameters for these models need to be manually set or, ideally, estimated from data. For GP regression, hyperparameters are typically estimated using prior data. This paper addresses the case where initial hyperparameters need to be estimated, but no prior data is available. Without prior data or accurately pre-defined hyperparameters, adaptive sampling techniques may fail, because there is no good model to base path planning decisions on. One method of gathering data is to perform a pilot survey. This survey needs to select informative samples for initiating the model, but without having a model to determine where best to sample. In this work, we evaluate four pilot surveys, which use a softmax function on the distance between waypoints and previously sampled data for waypoint selection. Simulation results show that pilot surveys that maximize waypoint spread over randomization lead to more stable estimation of GP hyperparameters, and create accurate models more quickly.

Autonomous underwater vehicles (AUVs) are cost- and time-efficient systems for environmental sampling. Informative adaptive sampling has been shown to be an effective method of sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative adaptive sampling. We use a dynamic Voronoi partitioning approach whereby the vehicles, in a decentralized fashion, repeatedly calculate weighted Voronoi partitions for the space. Each vehicle then runs informative adaptive sampling within their partition. The vehicles can request surfacing events to share data between vehicles. Simulation results show that the addition of the coordination with dynamic Voronoi partitioning results in obtaining higher quality models faster. Thus we created a decentralized, multi-robot coordination approach for informative, adaptive sampling of unknown environments.