Towards Autonomous Robot Navigation in Human Populated Environments Using an Universal SFM and Parametrized MPC

Autonomous mobile robot navigation in a human populated and encumbered environment is recognized as a hard problem to be solved in real-time. Most of the time, robots face the so-called ‘Freezing Robot Problem’, that occurs when the robot stops because no feasible and safe motion can be found. In order to provide to the robot the capability of proactive navigation, in this work we generalize the classical Social Force Model into a Universal Social Force Model (USFM) that attributes to any object surrounding the robot (humans, robots, obstacles) a social behavior. Nonlinear Model Predictive Control (MPC) can be used to solve the autonomous navigation problem since it can take into account all the possible constraints coming from the interaction model between the robot and the different surrounding objects. However, to be effective, MPC requires a sufficiently large prediction horizon, which generally implies a high computational cost. In order to considerably reduce the computational cost, we propose a new control parametrisation based on Thin Plate Spline Radial Basis Functions that allow us to have a large prediction horizon with fewer parameters. The global control framework is validated in simulation with virtual pedestrians, and in real world environments.

I. Introduction

The utilization of robots in human populated environment is becoming increasingly effective and significant. Some applications include floor cleaning (such as in supermarkets, subway stations, etc.), package transportation and delivery, and orientation guidance (such as in historical centers, museums, fairs, etc.). To achieve autonomous navigation in such environments, it is necessary to estimate the behavior of actors (including humans, moving obstacles, and other robots), compute real-time control while ensuring safety, and potentially perform proactive navigation (i.e., compute actions that are compatible and acceptable to humans while avoiding the Freezing Robot Problem [1]).