Reactive Planning for Assistive Robots

We consider a vehicle consisting of a robotic walking assistant pushed by a user. The robot can guide the person along a path and suggest a velocity by various means. The vehicle moves in a crowded environment and can detect the different pedestrian in the surroundings. We propose a reactive planner that modifies the path in order to avoid the pedestrian in the surroundings. The algorithm relies on a very accurate model to predict the motion of each pedestrian, i.e. the Headed Social Force Model (HSFM). The possible trajectories for both the vehicle and the pedestrians are modelled as clothoid curves, which are efficient to manage from the numeric point of view and are very comfortable to follow for the user. Probabilistic techniques are used to account for the variability of the motion of each pedestrian. The path is efficient to generate, is collision free (up to a certain probability) and is comfortable to follow. Simulations and comparisons with a state of the art planner using real data as well as experiments are reported to prove the effectiveness of the method.