I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

We present a motion planning algorithm to compute collision-free and smooth trajectories for robots cooperating with humans in a shared workspace. Our approach uses offline learning of human actions and their temporal coherence to predict the human actions at runtime. This data is used by an intention-aware motion planning algorithm that is used to compute a reliable trajectory based on these predicted actions. This representation is combined with an optimization-based trajectory computation algorithm that can handle dynamic, point-cloud representations of human obstacles. We highlight the performance of our planning algorithm in complex simulated scenarios and real world scenarios with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intentionaware planner in terms of computing safe trajectories.