  Predictive planning is a key capability for robots to efficiently and safely
navigate populated environments. Particularly in densely crowded scenes, with
uncertain human motion predictions, predictive path planning, and control can
become expensive to compute in real time due to the curse of dimensionality.
With the goal of achieving pro-active and legible robot motion in shared
environments, in this paper we present HuMAN-MPC, a computationally efficient
algorithm for Human Motion Aware Navigation using fast embedded Model
Predictive Control. The approach consists of a novel model predictive control
(MPC) formulation that leverages a fast state-of-the-art optimization backend
based on a sequential quadratic programming real-time iteration scheme while
also providing feasibility monitoring. Our experiments, in simulation and on a
fully integrated ROS-based platform, show that the approach achieves great
scalability with fast computation times without penalizing path quality and
efficiency of the resulting avoidance behavior.
