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 param- eters. The robots are randomly scattered across the terrain and collectively sam- ple 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.
Multi Robot Manipulation