A MODEL PREDICTIVE CONTROLLER FOR SOCIAL PERSON-FOLLOWING ROBOT FOR LONG-TERM INDOOR NAVIGATION

Abstract

The applications of shared autonomy or human-robot interaction are growing rapidly in the field of autonomous robotics. Assisting human beings in dynamically changing environments in urban areas is still an active area of research. In crowded scenarios, in a structured environment such as public places with occlusions and dynamic obstacles, moving vehicles, people and so on - is the most critical part of this challenge. And, our work focuses on developing effective control strategies using model predictive control (MPC) because it is best known for handling such uncertainty and complex system dynamics relatively easily. While the extensive use of data-driven techniques using machine learning has become the de facto solution today, the underlying physics, the model of a system, and its behaviour are neces- sary to develop control laws. We first design an innovative MPC controller for a social person follower that can move safely around humans. We further incorporate motion-planning, target-tracking, and so- cial norms into a single holistic framework, being the first of its kind on a differential drive-wheeled mobile robot. To develop this robust person following behavior, we also employ path prediction using LSTM (Long-Short Term Memory) a type of recurrent neural network for supervised learning. This allows us for out-of-sight tracking and natural anticipation of a person’s future state. We also develop a local-map-based early relocation (ER) strategy that can reduce oscillations in the path, maintain the field of view (FOV) for long-term indoor navigation. Thus, we move beyond trivial person following to anticipating future visit locations and following them in the present. Overall, a non-linear MPC-based control law is designed using an online optimization problem with constraints on both kinematics and dynamics, as well as social norms of safety around humans. We implement these using 2D simulations in Matlab, and in Python to test the controller performance, runtime analysis, and error analysis. We show that the MPC framework can run in real-time with an adequate margin for adapting to changing human movement patterns, and agile enough for its changing movement speeds.