Novel Method for Enhancing Warehouse Operations Using Heterogeneous Robotic Systems for Autonomous Pick-and-Deliver Tasks

The rapid rise of warehouse automation has increased the need for reliable multi-robot coordination. Efficient task allocation and path planning are central challenges that affect picking speed, energy use, and system scalability. This paper proposes an integrated framework for warehouse-oriented multi-robot task allocation and route planning. The method combines the Hungarian algorithm for cost-minimized task distribution with an open-loop Traveling Salesman Problem (TSP) for path sequencing. Unlike approaches that apply these steps separately, our framework links them in a single design and adds two practical extensions: explicit handling of heterogeneous robot capacities and a reassignment phase that recovers tasks left unallocated after the first assignment. These additions improve coverage and efficiency while keeping computation lightweight. Simulations in MATLAB show good scaling with larger fleets and reductions in both travel distance and execution time. The proposed framework provides a heterogeneity-aware allocation mechanism, robust unassigned-task handling, and integrated path optimization, and can be extended to dynamic order insertion and obstacle-aware navigation in warehouse settings.