According to CNN Indonesia 2020, the demand for e-Commerce in Indonesia has nearly doubled during this pandemic. This surge in demand calls for a time-efficient method for warehouse order-picking. One approach to achieve that goal is by incorporating automation in their warehouse systems. Globally, the market of warehouse robotics is expected to reach 12.6 billion USD by 2027 (Data Bridge Market Research, 2020). In this research, the warehouse system studied would utilize AMR (Autonomous Mobile Robots) to lift and deliver movable shelf units to the packing station where workers are at. This research designed a heuristic algorithm called A.N.T.s (Algorithm for Navigating Traffic System) to conduct task assigning and pathfinding for AMR in the automated warehouse. The warehouse layout was drawn as a two-dimensional map in grids. When an order is placed, A.N.T.s would assign the task to a robot that would require the least amount of time to reach the target shelf. A.N.T.s then conducted pathfinding heuristically using Manhattan Distance. A.N.T.s would help the robot to navigate its way to the target shelf unit, lift the shelf and bring it to the designated packing station. A.N.T.s algorithm was tested in various warehouse layouts and with a varying number of AMRs. Comparison against the commonly used Djikstra’s algorithm was also conducted (Shaikh and Dhale, 2013). Results show that the proposed A.N.T.s algorithm could execute 100 orders in a 27x23 layout with five robots 9.96 times faster than Dijkstra with no collisions. The algorithm is also shown to be able to help assign tasks to robots and help them find short paths to navigate their ways to the shelf units and packing stations. A.N.T.s could navigate traffic to avoid deadlocks and collisions in the warehouse with the aid of lanes and directions.