Recent technological advances have facilitated the use of mobile robots for a wide range of coverage applications such as inspection and mapping of infrastructure, precision agriculture, and disaster management. With the proliferation of these tasks comes an increasing need for autonomous systems to efficiently gather data pertinent for analyzing the state of the environment. The dissertation answers the following fundamental question: How should resource-constrained robots traverse the environment to collect data from all the relevant features? These features of interest can be represented as points, lines or curves, and areas. This dissertation unifies simultaneous coverage of all three types of features into a novel generalized coverage framework, develops algorithms for efficient coverage using multiple mobile robots, and validates them in experiments.
The dissertation comprehensively studies the line coverage problem, i.e., coverage of one-dimensional features, which lays the foundation of the generalized coverage problem. We develop algorithms to transform point and area features into linear features and use line coverage algorithms to solve generalized coverage efficiently. The algorithms substantially improve the state of the art while incorporating battery life constraints, nonholonomic constraints for robots that cannot take turns in place, and multiple home locations for large-scale environments.