UC Santa Cruz

Autonomous vehicles are becoming the platform of choice for large-scale exploration of environmental processes, owing to their low cost and dependability of sensors. Standard trajectory planning methods often preplan a trajectory in advance, or are based on information criteria, and do not use the observations taken at earlier locations on the trajectory to decide where the vehicle should go next. In this dissertation, we propose a framework for real-time, adaptive generation of trajectories that are optimal with respect to some exploration goal, with a focus on smooth continuous trajectories for nonholonomic vehicles. We develop an algorithm that enables the vehicle to gather data, reconstruct the environmental process, and generate piecewise optimal trajectory segments that use the data collected at previously visited locations. Our approach is based on Gaussian process priors and Bayesian optimal design. Gaussian processes provide a method to fuse any prior knowledge of the environmental process with the collected data to obtain the most updated estimation of the process. Through Bayesian optimal design, we develop reward functions that explicitly reflect the operational goal and naturally address the exploration-exploitation trade-off in a principled way. We include a number of empirical evaluations of the methodology and show the advantages of our algorithm on different archetypes of spatial processes. Field tests of the planner on an autonomous ground vehicle demonstrate the practical usefulness of our algorithm. We then extend the framework to incorporate supplementary information from different types of off-vehicle sensors. Three incorporation methods, depending on the nature of the supplementary source, are developed and tested, resulting in performance improvement, especially in the early stages of exploration. Finally, we turn to the case of multiple vehicles and develop an extension of our optimal trajectory algorithm that uses collective information to estimate the process and individual trajectory planning for each vehicle. As a result, computation time per vehicle does not increase as more vehicles are added, while the time required to achieve the exploration goal is reduced nearly linearly.