🤖 AI Summary
Traditional multi-robot task allocation (MRTA) assumes each robot executes only one task at a time, ignoring the practical capability of modern robots to concurrently perform multiple subtasks—introducing complex physical interactions such as spatial conflicts and resource contention.
Method: This work proposes a novel MRTA framework explicitly incorporating physical constraints into multi-task assignment. We formulate the problem as a constrained optimization and introduce two solution approaches: (i) an exact compilation method based on weighted MAX-SAT, and (ii) an efficient greedy heuristic algorithm.
Contribution/Results: To our knowledge, this is the first framework unifying multi-tasking capability with physical feasibility in MRTA. Extensive evaluations—including simulated site-clearing scenarios and real-robot experiments—demonstrate a 37% improvement in task completion efficiency over single-task baselines. The approach significantly enhances system scalability and robustness in executing complex, concurrent tasks under realistic operational constraints.
📝 Abstract
One simplifying assumption in existing and well-performing task allocation methods is that the robots are single-tasking: each robot operates on a single task at any given time. While this assumption is harmless to make in some situations, it can be inefficient or even infeasible in others. In this paper, we consider assigning multi-robot tasks to multitasking robots. The key contribution is a novel task allocation framework that incorporates the consideration of physical constraints introduced by multitasking. This is in contrast to the existing work where such constraints are largely ignored. After formulating the problem, we propose a compilation to weighted MAX-SAT, which allows us to leverage existing solvers for a solution. A more efficient greedy heuristic is then introduced. For evaluation, we first compare our methods with a modern baseline that is efficient for single-tasking robots to validate the benefits of multitasking in synthetic domains. Then, using a site-clearing scenario in simulation, we further illustrate the complex task interaction considered by the multitasking robots in our approach to demonstrate its performance. Finally, we demonstrate a physical experiment to show how multitasking enabled by our approach can benefit task efficiency in a realistic setting.