Generosity-Based Schedule Deconfliction in Communication-Limited Environments

We present an algorithmic approach to task deconfliction for teams of autonomous agents in communication-limited environments when the teams produce schedules with strong cross-schedule dependencies without the knowledge of the existence of other teams attempting to prosecute the same task set. Once agents discover others are attempting to prosecute the same task, they deconflict the task by trading assigned tasks. The method to determine the agents and tasks to trade in an efficient manner we term the Generous Agent Algorithm (GAA). Through formal analysis and simulation we show that teams utilizing the GAA can observe significant reductions in schedule end times and mission fuel requirements. We compare the GAA’s performance with a greedy deconfliction algorithm and a centralized scheduling algorithm to show that the GAA improves performance in situations when the number of agents is large within limited-communication environments.