Object transport has a lot of applications in industries and agriculture as well as disaster relief and warehouses applications. This poster presents a novel distributed algorithm for multi-robot systems to collectively transport a large object while avoiding obstacles in unknown environment. Given the size of the object, path planner robots generate the minimum cost path from start to the goal position by using a distributed Bellman-Ford algorithm. Then, transporter robots carry the object through the path. A transport is safe if it is obstacle free. We define transport cost as the cost of translation and rotation of the object. This study is trying to trade-off between the cost and safety of the transport by using distributed configuration space and tree-based path planing. We have implemented our algorithm both in simulation and real environments. As results show, our approach is robust to the size and shape of the object and provides a safe and efficient transport in unknown environments.
