Market-based coordination in dynamic environments based on the Hoplites framework

This work focuses on multi-robot coordination based on the Hoplites framework for solving the Multi-Robot Task Allocation (MRTA) problem. In particular, we investigate three variations of increasing complexity for the MRTA problem: spatial task allocation based on distance, spatial task allocation based on time and distance, and persistent coverage. The Fast Marching Method (FMM) has been used for robot path planning and providing estimates of the plans that robots bid on, in the context of the market. The use of this framework for solving the persistent coverage problem provides interesting insights by taking a high-level approach that is different from the commonly used solutions to this problem such as computing robot trajectories to keep the desired coverage level. A high fidelity simulation tool, Webots, along with the Robotic Operating System (ROS) have been utilized to provide our simulations with similar complexity to the real-world tests. Results confirm that this pipeline is a very effective tool for our evaluations given that our simulations closely follow the results in reality. By modifying the replanning to prevent having costly or invalid plans by means of priority planning and turn taking, and basing the coordination on maximum plan length as opposed to time, we have been able to make improvements and adapt the Hoplites framework to our applications. The proposed approach is able to solve the spatial task allocation and persistent coverage problems in general. However, there exist some limitations. Particularly, in the case of persistent coverage, this method is suitable for applications where moderate spatial resolutions are sufficient such as patrolling.