Force-Compliance MPC and Robot-User CBFs for Interactive Navigation and User-Robot Safety in Hexapod Guide Robots

2025-08-05

AI Summary

To address real-time bidirectional interaction and human–robot safe navigation for hexapod guide robots in complex dynamic environments, this paper proposes an integrated framework combining force-admittance model predictive control (MPCC) with robot–user collaborative control barrier functions (CBFs). To resolve feasibility conflicts among multiple safety constraints, weighted slack variables are innovatively introduced. User-intended forces/torques are estimated online via recursive least squares, enabling adaptive force-admittance control. Obstacles are modeled using an eight-neighborhood DBSCAN clustering algorithm coupled with minimum bounding ellipses, while trajectory prediction is enhanced by Kalman filtering. Experimental validation on the HexGuide platform demonstrates millisecond-level command response, dynamic obstacle avoidance, and end-to-end physical safety assurance. The proposed approach significantly improves navigation autonomy, robustness, and user trust for visually impaired individuals.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Guiding the visually impaired in complex environments requires real-time two-way interaction and safety assurance. We propose a Force-Compliance Model Predictive Control (FC-MPC) and Robot-User Control Barrier Functions (CBFs) for force-compliant navigation and obstacle avoidance in Hexapod guide robots. FC-MPC enables two-way interaction by estimating user-applied forces and moments using the robot's dynamic model and the recursive least squares (RLS) method, and then adjusting the robot's movements accordingly, while Robot-User CBFs ensure the safety of both the user and the robot by handling static and dynamic obstacles, and employ weighted slack variables to overcome feasibility issues in complex dynamic environments. We also adopt an Eight-Way Connected DBSCAN method for obstacle clustering, reducing computational complexity from O(n2) to approximately O(n), enabling real-time local perception on resource-limited on-board robot computers. Obstacles are modeled using Minimum Bounding Ellipses (MBEs), and their trajectories are predicted through Kalman filtering. Implemented on the HexGuide robot, the system seamlessly integrates force compliance, autonomous navigation, and obstacle avoidance. Experimental results demonstrate the system's ability to adapt to user force commands while guaranteeing user and robot safety simultaneously during navigation in complex environments.

Problem

• Ensuring real-time two-way interaction for visually impaired guidance
• Achieving force-compliant navigation and obstacle avoidance in Hexapod robots
• Guaranteeing user and robot safety in complex dynamic environments

Innovation

• Force-Compliance MPC for two-way interaction
• Robot-User CBFs ensure safety dynamically
• Eight-Way DBSCAN reduces computational complexity