Collaborative Human-Robot Object Transportation Using a Deformable Sheet

In this paper, we tackle real-time formation trajectory planning for collaborative object transportation in complex environments using a team of nonholonomic robots and a human. The object is transported in a deformable sheet, and robots should follow the human’s lead while autonomously avoiding obstacles. By including a human in the formation, we leverage their adaptability and decision-making to improve transportation. However, it can be difficult for a human to predict how autonomous robots will behave in complex situations, such as when the formation must cross an obstacle, i.e. where the object is transported above it. This could cause human decisions that compromise safety. To overcome these challenges, we introduce a multi-modal formation planning framework. By default the human leads the formation, and the robots plan to remain in the same homotopy class as the human to avoid collisions. If obstacle crossing is necessary the robots take the lead of the formation, where human motion is constrained to a feasible region projected visually in front of them. We demonstrate the efficacy of our framework in simulation and on hardware.