Search papers, labs, and topics across Lattice.
This paper introduces OLAhGP, a Gaussian process-based path planning method that integrates multi-modal environmental sensing data and state/input constraints for robotic exploration. The method optimizes future waypoints in a receding horizon, using the Gaussian process posterior as the cost function. Experiments on an autonomous surface vessel using algal bloom data demonstrate that OLAhGP generates more informative paths and achieves greater accuracy in identifying algal blooms compared to existing methods, as measured by misclassification metrics.
Autonomous robots can now explore and map environments more effectively by using Gaussian processes to plan paths that adapt to multi-modal sensor data and uncertainty.
Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environmental exploration and monitoring depends highly on the current belief of the world. To capture the uncertainty in the belief, we present a Gaussian process based path planning method that adapts to multi-modal environmental sensing data and incorporates state and input constraints. To solve the path planning problem, we optimize over future waypoints in a receding horizon fashion, and our cost is thus a function of the Gaussian process posterior over all these waypoints. We demonstrate this method, dubbed OLAhGP, on an autonomous surface vessel using oceanic algal bloom data from both a high-fidelity model and in-situ sensing data in a monitoring scenario. Our simulated and experimental results demonstrate significant improvement over existing methods. With the same number of samples, our method generates more informative paths and achieves greater accuracy in identifying algal blooms in chlorophyll a rich waters, measured with respect to total misclassification probability and binary misclassification rate over the domain of interest.
