# Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach

## TL;DR

This paper develops a hierarchical hybrid systems control framework for safe, cooperative locomotion of robotic guide dogs and visually impaired humans, integrating nonlinear control, safety guarantees, and real-time obstacle avoidance.

## Contribution

It introduces a novel hybrid systems approach combining nonlinear control, safety-critical control barrier functions, and real-time quadratic programming for cooperative robot-human locomotion.

## Key findings

- Successfully designed stable gaits using virtual constraints.
- Implemented real-time obstacle avoidance with control barrier functions.
- Validated the approach through extensive numerical simulation of a complex quadrupedal robot-human model.

## Abstract

This paper presents a hierarchical control strategy based on hybrid systems theory, nonlinear control, and safety-critical systems to enable cooperative locomotion of robotic guide dogs and visually impaired people. We address high-dimensional and complex hybrid dynamical models that represent collaborative locomotion. At the high level of the control scheme, local and nonlinear baseline controllers, based on the virtual constraints approach, are designed to induce exponentially stable dynamic gaits. The baseline controller for the leash is assumed to be a nonlinear controller that keeps the human in a safe distance from the dog while following it. At the lower level, a real-time quadratic programming (QP) is solved for modifying the baseline controllers of the robot as well as the leash to avoid obstacles. In particular, the QP framework is set up based on control barrier functions (CBFs) to compute optimal control inputs that guarantee safety while being close to the baseline controllers. The stability of the complex periodic gaits is investigated through the Poincare return map. To demonstrate the power of the analytical foundation, the control algorithms are transferred into an extensive numerical simulation of a complex model that represents cooperative locomotion of a quadrupedal robot, referred to as Vision 60, and a human model. The complex model has 16 continuous-time domains with 60 state variables and 20 control inputs.