Risk-Informed-RRT*: A Sampling-based Human-friendly Motion Planning Algorithm for Mobile Service Robots in Indoor Environments

The pedestrians in human-robot coexisting environments introduce many challenges to the motion planning of mobile service robots. Besides the problems of minimizing the runtime of the robot navigation, the safety and psychological comfort of the pedestrians should be also guaranteed. This paper presents an optimal sampling-based motion planning algorithm for the human-robot coexisting environments, which models the psychological comfort of the pedestrians by constructing the Estimated Comfort and Collision Risk (ECCR) map of the working environment, and reduces the runtime of the robot navigation by adopting the optimal sampling method in the Informed-RRT* algorithm. The ECCR map combines the pose estimations and the psychological comfort estimations with the static map of the environment. By applying the ECCR map in the motion planning process, the robot can not only avoid collisions with the pedestrians but also keep enough distance to ensure their psychological comfort. The navigation runtime and the lengths of the trajectories are recorded for the evaluations in this work. The experimental results in both simulations and real-world experimental studies reveal that the proposed Risk-Informed-RRT* algorithm can achieve a better performance than the Risk-RRT algorithm in both static and dynamic environments.