Social robot motion planning using contextual distances observed from 3D human motion tracking

Abstract

The social robot motion planning involves robots operating within the workspace and alongside humans and it is thus necessary for the robots to behave socially. The current literatures make trajectory databases to learn the human trajectory, which has a limited application since only very few out of large possibilities can be recoded. Another approach to the problem is by modelling the macroscopic behaviors, which is limited by parameters that are practically hard to set. Our approach to the problem is thus to understand human motion through a set of simple and well-known primitives, which are observed from the actual human motion. In this paper, we first perform human detection and tracking in a 3D environment. We use a stationary 3D Lidar sensor for the detection and tracking of all moving humans. Our approach detects all moving people and it also solves the problem of occlusion in several cases. We further consider several research hypotheses regarding human navigation noting how much distances the humans prefer to maintain with other humans and obstacles under different common scenarios. The experimentation is done with several subjects and their behavior is used to answer the research hypotheses. A Social Robot Motion Planning algorithm is developed by using a social potential field algorithm as a base. New social forces are added to model the different behaviors displayed by humans. The motion planning algorithm makes the robot maintain the same distances as observed with the humans and perturbating the robots makes them reach the equilibrium distance again.