Data-Driven Multi-Robot Planning for Online User-Directed Coordination
Creators: name: Cappo, Ellen
Publisher: name: Carnegie Mellon University
Year: 2021
Subjects: subject: 80101 Adaptive Agents and Intelligent Robotics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Multi-robot applications frequently seek to employ human operators to direct robot actions online because fully automated planners struggle to encode human expertise orhandle the extenuating circumstances that occur during real world operations. However, it is extremely challenging for a human to direct multi-robot teams, especially online, i.e., in real-time. From entertainment to defense, applications can require detailed inter-robot coordination direction which can be difficult for an operator to specify. Finding motion plans that then meet user intent is complicated by the need tooperate in cluttered environments while absolving the human operator from having to consider physical constraints of the system such as dynamic feasibility or safety.Existing multi-robot policy specification and planning methods have difficulty providing all the required capabilities to enable high-speed, online, human-directed multiagentmotion generation in cluttered environments. While reaction based or local control methods have been widely used for multi-robot applications due to their fast computation times, these methods can fail to produce coordinated responses or safety guarantees. In contrast, optimization methods and search approaches are frequently used to coordinate actions across robots and generate high quality motion plans. Unfortunately, the large number of inherent decision variables, especially as frequently incurred when considering numerous obstacles, often means these methods require computation times in excess of online operation limits. In this thesis, we enable a search based methodology to find safe and feasible multirobottrajectories at fast time scales even in highly cluttered environments by leveraging a group-based representation of multi-robot actions and offline acquired data. Ourapproach generalizes across environments and enables an operator to coordinate multirobot motions online for applications requiring high-frequency plan generation such as required by joystick control. We show results for two human-commanded multi-robot applications: theatrical performance and urban reconnaissance. Both applications require a multi-robot planner to resolve differences between operator input and feasibility requirements given the current state of the system, and generate dynamically feasible and safe trajectories for all agents within time and computation constraints. This thesis establishes an online multi-robot planning and control system for the specification of multi-robot behaviors, and we demonstrate the feasibility of the approach using a multi-quadrotor system, controlling up to 30 physical robots online in the context of the theatric application. We then address assumptions made under thetheatric context to meet the high-frequency planning and cluttered environment context of the urban reconnaissance application, and present a generalized formulation ofmulti-robot behaviors and leverage examples of multi-agent actions from real world data sets to inform an online search policy. We demonstrate that the generalized representationcoupled with the proposed data-based search heuristic enables high-speed multi-robot coordination in cluttered environments, providing needed capabilities to enable multi-robot solutions for complex real world applications.