We present a scalable distributed path planning algorithm for transport- ing a large object through an unknown environment using a group of homogeneous robots. The path is optimal given the sampling of the robots and user input parameters. The robots are randomly scattered across the terrain and collectively sample the environment in a distributed fashion. Using the dimensions of the payload, the robots first construct a configuration space. With a variant of the distributed Bellman-Ford algorithm, we then construct a shortest-path tree using a custom cost function from the goal location to all other connected robots. The cost function en- compasses the work required to rotate and translate the object in addition to an extra control penalty to navigate close to obstacles. Our approach sets up a framework that allows the user to to balance the trade-off between the safety of the path and the mechanical work required to move the object. We implemented our algorithm in both simulated and real-world environments. Our approach is robust to the size and shape of the object and adapts to dynamic environments.