ScRATCHS: Scalable and Robust Algorithms for Task-Based Coordination from High-Level Specifications

Austin M Jones, Kevin Leahy, Cristian Ioan Vasile, Sadra Sadradinni, Zachary Serlin, Roberto Tron, and Calin Belta. Scalable and Robust Deployment of Heterogenenous Teams from Temporal Logic Specifications. In International Symposium on Robotics Research (ISRR), Hanoi, Vietnam, October 2019.

Published date: 
Wednesday, October 9, 2019

Existing approaches for coordinating teams of heterogeneous agents either consider small numbers of agents, are application-specific solutions, or do not adequately address requirements, e.g., deadlines or inter-task dependencies,common to real-world applications. We develop a framework called Scalable and Robust Algorithms for Task-based Coordination from High-level Specifications(ScRATCHS) to coordinate such teams. We define a specification language, called Capability Temporal Logic (CaTL), to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities,e.g., sensors or end effectors. An example specification is "Ensure at least 10 airborne cameras and 3 airborne lidars are surveying Site A for at least 15 minutes simultaneously during every hour-long period. Make sure that 5 cameras are always observing Site B. Send 10 lidars to Site B within 3 hours of deployment and remain there until 4 ground vehicles with infrared sensors arrive 2 hours later." Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program (MILP), which can be solved efficiently using commercial off-the-shelf solvers. ScRATCHS also enables optimization of the resulting plan to be maximally robust to agent attrition at the penalty of increased computation time. The flexible specification language, fast solution time, and optional robustness of ScRATCHS provide a first step towards a multi-purpose on-the-fly planning tool for a supervisor tasking large teams with multiple capabilities enacting missions with multiple tasks. We validate our approach using randomized computational experiments and via a hardware demonstration.