A scalable multi-robot task allocation algorithm

In modern warehouses, robots are being deployed to perform complex tasks such as fetching a set of objects from various locations in a warehouse to a docking station. This requires a careful task allocation along with route planning such that the total distance traveled (cost) is minimized. The number of tasks that can be performed by a robot on a single route depends on the maximum capacity of the robot and the combined weight of the objects it picks on the route. This task allocation problem is an instance of the Capacity-constrained Vehicle Routing Problem (CVRP), which is known to be NP-hard. Although, there exist a number of heuristics that provide near-optimal solutions to a CVRP instance, they do not scale well with the task size (number of nodes). In this paper, we present a heuristic, called nearest-neighbor based Clustering And Routing (nCAR), which has better execution time compared to the state-of-the-art heuristics. Also, our heuristic reduces cost of the solutions when there are a large number of nodes. We compare the performance of nCAR with the Google OR-Tools and found a speedup of 6 in runtime when task size is 2000. Though OR-Tools provides a low-cost solution for small number of tasks, it's execution time and number of routes is 1.5 times that of nCAR.