Assisted Mobile Robot Teleoperation with Intent-aligned Trajectories via Biased Incremental Action Sampling

We present a method to assist the operator in teleoperation of mobile robots by generating trajectories such that the vehicle completes the desired task with ease in unstructured environments. Traditional assisted teleoperation methods have focused on reactive methods to avoid collisions, but neglect the operator's intention in doing so. Instead, we generate long horizon, smooth trajectories that follow the operator's intended direction while circumventing obstacles for a seamless teleoperation experience. For mobile robot teleoperation, an explicit goal in the state space is often unclear in cases such as exploration or navigation. Therefore, we model the intent as a direction and encode it as a cost function. As trajectories of various lengths can satisfy the same directional objective, we iteratively construct a tree of sequential actions that form multiple trajectories along the intended direction. We show our algorithm on a real-time teleoperation task of a simulated hexarotor vehicle in a dense random forest environment. By doing so, our approach allows operator to achieve the navigation task while requiring less effort than reactive methods.