Effective Multi-Robot Spatial Task Allocation using Model Approximations
This work addresses spatial task allocation for multi-robot systems in rescue simulations, offering incremental improvements in speed and performance over existing methods.
The paper tackled the problem of multi-robot spatial task allocation in complex environments like firefighting rescue simulations by approximating it as a multi-agent Markov decision process and applying methods such as task clustering and dynamic planning horizons. The results showed that the approach is faster and better than comparable methods with negligible performance loss compared to the optimal policy, achieving similar performance to DCOP methods in example scenarios.
Real-world multi-agent planning problems cannot be solved using decision-theoretic planning methods due to the exponential complexity. We approximate firefighting in rescue simulation as a spatially distributed task and model with multi-agent Markov decision process. We use recent approximation methods for spatial task problems to reduce the model complexity. Our approximations are single-agent, static task, shortest path pruning, dynamic planning horizon, and task clustering. We create scenarios from RoboCup Rescue Simulation maps and evaluate our methods on these graph worlds. The results show that our approach is faster and better than comparable methods and has negligible performance loss compared to the optimal policy. We also show that our method has a similar performance as DCOP methods on example RCRS scenarios.