Numerical simulations and comparative study for auction-based sample gathering solutions

This work answers the question “Which robot is best suited to perform a certain task?” by implementing the auction concept into different types of algorithms. This idea outlines a framework where an auctioneer presents tasks to buyers, which submit bids for accomplishing them based on the required costs. The winner is determined through a designed mechanism, after a full analysis of all received bids. The purpose of this work is to plan a team of mobile agents to gather the samples that exist in the work environment at known locations by applying the auction-based task allocation scheme. The static environment is modeled by a graph, where an edge weight represents the distance between the linked nodes. Given the initial locations of robots and samples, the tasks allocations are determined before the agents start to move. The resulted solutions are exemplified in the Sim.I.am simulator on a particular work space.