Multi-Constrained Voronoi-Based Task Allocator for Smart-Warehouses

Abstract

Multi-robot systems consist of a set of robots working together to achieve a common goal. In these systems, two types of problems are widely addressed: the multi-robot path planning (MPP) and the multi-robot task allocation (MRTA). While the first one consists of finding the best path between two points in the space, the second one consists of allocating tasks to the robots, meeting restrictions, and completing one or more general objectives. The objectives are usually related to time optimization and energy consumption. The constraints require attention because they impact the complexity of the problem and reduce the system’s performance. Smart warehouses are an important example of application in which these problems are relevant. In such applications, the picking and shipping products control happens in an automated way, and mobile robots completely operate them. The literature shows that few studies explore integrated MPP and MRTA strategies to solve task allocation restrictions in smart warehouses. The main contribution of this paper is to present an integrated MRTA and MPP approach for smart warehouses by using static, seasonal, and dynamic information. Static information is provided by fixed obstacles, battery level, and load capacity of the robots. Seasonal information comes from products’ location and availability of them in a given period. The dynamic information corresponds to battery consumption and dynamic obstacles. In this paper a Multi-Constraints Voronoi-based Task Allocator (MCVB-TA) is presented. Its implementation incorporates a variation of the Voronoi diagram to allocate robots to the nearest tasks according to constraints, robots, and the environment. The simulation results obtained show that the proposed solution considerably reduces the time and energy cost of executing tasks in a smart warehouse scenario compared to a regular scheduler.