Followman: Control of Social Person Following Robot

Exploring shared autonomy in assistive and service robots makes human-aware navigation of a person following robot (PFR) a required behaviour. In this paper, we propose a control framework for a non-holonomic wheeled robot that not only tracks a target person in sight but also anticipates the human movement patterns to predict the future sequence of its path when the person is out of sight. Human beings form a crowd and can exhibit complex random movement compared to vehicles while indoor environments with intersections can pose serious challenges for long-distance path-following without the breakdown of tracking. Thus, a nonlinear model predictive controller is designed with long-term prediction and socially compliant rules for natural person-following behaviour. It can generate the collision-free path and optimized control inputs using a single optimization framework Finally, the integrated navigation-control stack is evaluated using simulations for real-time operation. We also present a hardware configuration for its real-world implementation.