Coordinated autonomous systems: optimized motion in dynamic and uncertain environments

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Recent years have witnessed incredible advances in portable computation, sensing, and actuation. We are now capable of creating ground, aerial, and underwater robots that can move through complex environments, acquire rich sensor data, and process the data on-board to reason, navigate, and act. Applications of such robots are occurring in several domains. In environmental monitoring, underwater robots are increasing our understanding of oceanographic phenomena; in material handling, large fleets of robots are automating warehouses; and, in the military, uninhabited aerial vehicles are transforming the way in which reconnaissance missions are performed. Despite these advances, the level of autonomy in most applications is quite low. In many instances, human operators perform low-level tasks, such as remote operation. In areas such as automated warehouses, robots are required to operate in highly structured environments. These approaches are used because we do not currently have algorithms for planning reliable and efficient motion in dynamic and uncertain environments. This research program will develop control algorithms that enable robots to 1) plan motion based on incomplete environmental information, and 2) adapt motion plans as new information becomes available. We will focus on certifying that the algorithms produce robot behaviours that satisfy desired specifications, even in the presence of environmental uncertainty. The research will target three main applications areas: 1) environmental monitoring, where groups of robots must collect information about dynamic environments; 2) emergency response, where robots must discover and respond to urgent events; and 3) automated material handling, where robots must transport items in complex environments.