Stochastic Model Predictive Control with Optimal Linear Feedback for Mobile Robots in Dynamic Environments

TL;DR

This paper introduces an enhanced stochastic model predictive control approach with optimal linear feedback for mobile robot navigation in dynamic, uncertain environments, improving trajectory tracking and safety around humans.

Contribution

It integrates state feedback into stochastic MPC, allowing better trajectory tracking and collision avoidance in unpredictable human environments, with manageable computational costs.

Findings

Feedback improves trajectory tracking accuracy.

The method effectively bounds collision probability.

Computational overhead can be minimized with small performance trade-offs.

Abstract

Robot navigation around humans can be a challenging problem since human movements are hard to predict. Stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance.