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When a robot has to interact with a person in a dynamic environment, it has to navigate to reach a close distance and to be in front of the person. This navigation has to be smooth and take care of the person’s movements, the static obstacles and the motion of other people. In this paper, we present a new method to approach a person, that combines \(G^2\)-Splines (\(G^2\)S) paths with the Extended Social Force Model (ESFM) to allow the robot to move in dynamic environments avoiding static obstacles and other people. Moreover, we use the Bayesian human motion intentionally prediction (BMP) in combination with the Social Force Model (SFM) to be able to approach a moving person and also to avoid moving people in the environment. The method computes several paths using the \(G^2\)S and taking into account the person’s position and orientation. Then, the method selects the best path using several costs that consider distance, orientation, and interaction forces with static obstacles and moving people. Finally, the robot is controlled with the ESFM to follow the best path. The method was validated by a set of simulations and also by real-life experiments with a humanoid robot in a dynamic environment.

Robot Navigation to Approach People Using $$G^2$$-Spline Path Planning and Extended Social Force Model