Omnidirectional walking robot improves safety and energy use in obstacle tasks
Safety-Constrained Model Predictive Control for an Omnidirectional Walking Assistive Robot Using Control Barrier Function
Robotics
Summary
Helping people with motor difficulties move safely and comfortably is important. The authors designed a robot that helps users walk in any direction, using smart controls to avoid bumping into things and to save energy. They tested their new control system with people walking through tricky paths and avoiding unexpected objects. Results showed this method lowered energy use and collisions while keeping smooth movement, suggesting a safer, more efficient walking aid.
What this means in practice
- •For rehabilitation engineers: Develop walking assistive robots that reduce collisions and energy use during complex navigation tasks for users with motor impairments.
- •For mobile robot developers: Integrate Control Barrier Functions into predictive control schemes to enhance real-time safety and efficiency in omnidirectional navigation.
Authors
Andrea Fortuna, Marta Lorenzini, Elisa Motta, Alberto Ranavolo, Elena De Momi, Arash Ajoudani
Abstract
Providing safe and effective mobility assistance plays a crucial role in restoring independence and enhancing the quality of life for individuals with motor impairments. In this context, robotic walking assistive devices have recently emerged as promising solutions to provide physically compliant interaction while ensuring user safety and support. This paper presents a novel control framework for an omnidirectional Walking Assistive Robot (I-WANDER) that integrates a Control Barrier Function (CBF) formulation into a Model Predictive Control (MPC) scheme to explicitly enforce collision-avoidance safety constraints while optimizing for energy efficiency and smooth human-robot collaboration. The method was experimentally evaluated with 12 healthy participants performing two different walking tasks using both the proposed CBF-based MPC controller (CB-MPC) and a variable admittance controller (AC). The first task involved structured navigation through a U-shaped corridor, whereas the second consisted of a single-obstacle avoidance task performed blindfolded to ensure the obstacle was unexpected. Comparative results show that the CB-MPC architecture significantly reduces energy consumption and mechanical work (p < 0.01) without compromising motion smoothness, while also decreasing the number of obstacle collisions. Overall, the findings highlight the potential of the proposed control architecture to enhance both safety and efficiency in robotic walking assistance.