Guide Robot Control With User-State Fusion and Obstacle Avoidance
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Solution Overview
Problem
Existing robotic guide dog technologies assume a vision-impaired person follows the robot's path exactly, focusing solely on the robot's perception, planning, and control, without considering the user's state or obstacle avoidance, which limits their effectiveness and practicality.
Innovation Solution
A method and apparatus for controlling a guide robot that acquires the state of both the robot and the user, generates a state update equation for a combined system, and computes a collision-free global path to ensure obstacle avoidance, using sensors mounted on the robot and model predictive control to drive the robot's movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing robotic guide dog technologies focus solely on the robot's perception, planning, and control assuming the user follows the path exactly, then the robot's navigation capability is improved, but the system's reliability and practicality deteriorate due to lack of obstacle avoidance consideration
Solution Approach 1:
The patent merges the robot's navigation system with the user's motion model into a unified combined system model. This integration allows the system to simultaneously optimize for navigation efficiency while ensuring obstacle avoidance, resolving the contradiction between speed and reliability by treating them as coupled objectives rather than conflicting ones.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor both the robot's state and the user's state, and use this information to dynamically adjust the control commands. This feedback loop ensures that the system maintains reliable obstacle avoidance while achieving efficient navigation, as the control strategy adapts based on real-time system state information.
2Device complexity
If the system only considers the robot's state for path planning, then the control complexity is reduced, but the adaptability to user needs and actual environment deteriorates
Solution Approach 1:
The patent segments the overall control problem into distinct components: robot state modeling, user state modeling, and integrated control command generation. By dividing the complex problem into manageable segments that can be processed separately and then combined, the system achieves high adaptability to user needs while keeping the computational complexity tractable through modular processing.
Solution Approach 2:
The patent employs dynamic modeling that adapts to varying user states and environmental conditions. The system transitions from static, pre-programmed paths to dynamic, real-time adaptive control that responds to changing user needs and obstacle positions, thereby achieving high versatility without overwhelming complexity through efficient dynamic adjustment mechanisms.
3Device complexity
If the system assumes complete user compliance with the robot's path, then the path planning simplicity is improved, but the measurement precision of user position and obstacle detection deteriorates
Solution Approach 1:
The patent replaces the mechanical assumption of perfect user compliance with a computational model that predicts user position based on the user state model. This substitution allows the system to maintain simple path planning logic while achieving accurate user position estimation through mathematical modeling, thereby resolving the contradiction between planning simplicity and measurement precision.
Data Source
AI summary
Embodiments of the present disclosure disclose a method, apparatus, device, and storage medium for controlling a guide robot, and relate to the field of artificial intelligence, robots, and multi-sensor fusion technologies. A specific embodiment of the method includes: acquiring a state of the guide robot, a state of a user, and a position of an obstacle; generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user; generating a collision-free global path based on the position of the obstacle; generating a control command based on the state update equation for the combined system and the collision-free global path; and driving the guide robot to move based on the control command.