Autonomous person pacing and following with Model Predictive Equilibrium Point Control

The ability to follow or move alongside a person is a necessary skill for an autonomous mobile agent that works with human users. To accomplish the task, the robot must be able to track and follow the person it is accompanying while maneuvering through obstacles without collision. Also, the robot must be able to respect user preferences and exhibit behaviors that are intuitive and socially acceptable. That is, the robot is required to make complex decisions on-line, in environments that are almost always dynamic and uncertain in the presence of pedestrians. This paper discusses a versatile motion planning algorithm for person pacing, which refers to the capability to walk next to another person at user-preferred distance and orientation [1]. The algorithm is based on the Model Predictive Equilibrium Point Control (MPEPC) framework [2] which allows a robot to navigate gracefully in dynamic, uncertain, and structured environments. We show that with a simple task description for person pacing, an agent with the MPEPC navigation algorithm can make intelligent decisions on-line, maximizing the expected progress toward achieving the task while minimizing the action cost and the probability of collision. We present navigation examples generated from real data traces, where a wheelchair robot exhibits very reasonable behaviors across a wide range of situations.