Slides

We present distributed algorithms for transporting a large object through an unknown environment using a group of homogeneous robots. Our algorithm has three steps: 1- Using the dimensions of the object, the robots construct a configuration space of the object. These robots are randomly scattered across the terrain and collectively sample the environment in a distributed fashion. 2- Using a distributed Bellman-Ford algorithm, the shortest-path tree is constructed based on a cost function from the goal location to all other connected robots. The cost function encompasses the work required to rotate and translate the object in addition to an extra control penalty to navigate close to obstacles 3- Four distributed motion controllers are presented to transport the object along the generated path. These controllers include rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation towards a guide robot, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path.

We implemented our path planning algorithm in both simulated and real-world environments. Our path planning algorithm is robust to the size and shape of the object and adapts to dynamic environments. Our controllers are fully distributed and robust to changes in network topology, robot population, and sensors error. We also test our motion controllers in real-world environments with 9 robots, and show that our estimation of the centroid is accurate for different shape of objects. The result also shows all four controllers produce reliable motion of the object.