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 other pedestrians in the surroundings. We propose a reactive planner that modifies the path in order to avoid pedestrians 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. The possible trajectories for both the vehicle and the pedestrians are modeled 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.

Motion planning for service robots used for human assistance in complex environments is a challenging task with multifaceted requirements. On the one hand, the generated path has to avoid obstacles and satisfy the user requirements. On the other, the generated path has to be perceived as comfortable by the user. An efficient solution to this difficult problem is based on a separation of concerns between two subproblems: 1. generating a sequence of waypoints that connects the source to the destination, 2. generating a path that joins the waypoints and complies with the dynamic constraints. In this paper, we concentrate on the second problem with an explicit focus on how to account for users' comfort. The result is accomplished by solving an optimal control problem that inherently generates the smooth trajectories required by the user. The cost functions used in the optimisation problem allows to take different dimensions of the user's comfort into consideration. Our technique lends itself to an efficient numeric solution and can be easily applied to different algorithms for waypoint generation.