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

Like computer architects, robot designers must address multiple, possibly competing, requirements by balancing trade-offs in terms of processing, memory, communication, and energy to satisfy design objectives. However, robot architects currently lack the design guidelines, organizing principles, rules of thumb, and tools that computer architects rely upon. This thesis takes a step in this direction, by analyzing the roles of heterogeneity and distribution in robot systems architecture. This thesis takes a systems architecture approach to the design of robot systems, and in particular, investigates the use of distributed, heterogeneous platforms to exploit locality in robot systems design. We show how multiple, distributed heterogeneous platforms can serve as general purpose robot systems for three distinct domains with different design objectives: increasing availability in a search and rescue mission, increasing flexibility and ease-of-use for a personal educational robot, and decreasing the computation and sensing resources necessary for navigation and foraging tasks.

The objective of this thesis is to develop a method for the reconfiguration of three-dimensional modular robots. A modular robot is composed of simple individual building blocks or modules. Each of these modules needs to be controlled and actuated individually in order to make the robot perform useful tasks. The presented method allows us to reconfigure arbitrary initial configurations of modules into any pre-specified target configuration by using graph grammar rules that rely on local information only. Local in a sense that each module needs just information from neighboring modules in order to decide its next reconfiguration step. The advantage of this approach is that the modules do not need global knowledge about the whole configuration. We propose a two stage reconfiguration process composed of a centralized planning stage and a decentralized, rule-based reconfiguration stage. In the first stage, paths are planned for each module and then rewritten into a ruleset, also called a graph grammar. Global knowledge about the configuration is available to the planner. In stage two, these rules are applied in a decentralized fashion by each node individually and with local knowledge only. Each module can check the ruleset for applicable rules in parallel. This approach has been implemented in Matlab and currently, we are able to generate rulesets for arbitrary homogeneous input configurations.