| Summary: | As autonomous systems develop an ever expanding range of capabilities, monolithic systems (systems with multiple capabilities on a single platform) become increasingly expensive to build and vulnerable to failure. A promising alternative to these monolithic systems is a distributed team with different capabilities that can provide equivalent or greater overall functionality through cooperation. Such systems benefit from decreased individual system cost, robustness to partial system failure, and the possibility of operating over larger geographical areas. However, these benefits come at the cost of increased planning, control, perception, and computational complexity, as well as novel algorithm development. This thesis contributes to the start-of-the-art in distributed systems by drawing on techniques from the fields of formal methods to address problems in team task and motion planning, and from computer vision to address problems in multi-robot perception (specifically multi-image feature matching). These problems arise in persistent surveillance, robotic agriculture, post-disaster search and rescue, and autonomous driving applications.
Overall, this work enables resilient hierarchical planning for robot teams and solves the distributed multi-image feature matching problem, both of which were previously intractable to solve in many cases. We begin by exploring distributed multi-image feature matching for distributed perception and object tracking for a robot team or camera network. We then look at homogeneous multi-agent planning from rich infinite-time specifications that includes a secondary objective of optimizing local sensor information entropy. Next, we address heterogeneous multi-agent task planning from rich, timed specifications based on agent capabilities, and then detail mechanisms for online replanning due to agent loss. Finally, we address safe, reactive, and timed motion planning subject to temporal logic constraints. Accompanying each topic are a number of simulations and experiments that demonstrate their utility on real hardware. Overall, this thesis focuses on four primary contributions: 1) distributed multi-image feature matching, 2) motion planning for a homogeneous robotic team subject to distributed sensing and temporal logic constraints, 3) task planning for a heterogeneous robotic team with reactivity to changing agent availability, and 4) safe motion planning for an individual system that is reactive to disturbances and satisfies timed temporal logic constraints. === 2022-09-30T00:00:00Z
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