Dynamic task allocation system for wheel-legged hybrid robot based on model predictive control

The model predictive control-based dynamic task allocation system for wheeled-legged hybrid robots solves the problem of balancing endurance and motion performance in complex task scenarios, achieving intelligent energy consumption balance and efficient control in complex terrain and variable tasks.

CN122131822APending Publication Date: 2026-06-02HARBIN INST OF TECH AT WEIHAI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HARBIN INST OF TECH AT WEIHAI
Filing Date
2026-03-18
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

The fixed control strategies of existing wheeled robots are difficult to dynamically adjust energy consumption distribution when task requirements and environmental changes occur, making it difficult to balance endurance and motion performance in complex task scenarios.

Method used

A dynamic task allocation system for a wheeled-legged hybrid robot based on model predictive control is adopted, including an environmental perception and terrain classification module, an online parameter identification and state estimation module, a task decision and energy management module, and a multi-objective predictive controller. By fusing environmental information through stereo vision and lidar, the system dynamically adjusts energy allocation using an extended Kalman filter and a fuzzy inference system, and achieves composite motion by combining the Newton-Euler equation and the pseudo-inverse of the Jacobian matrix.

Benefits of technology

Achieving an intelligent balance between robot motion performance and energy consumption under complex terrain and varied task requirements significantly extends working time, adapts to diverse operational scenarios, and improves control accuracy and system reliability.

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Abstract

This invention relates to the field of robotics, and more particularly to a dynamic task allocation system for a wheeled-legged hybrid robot based on model predictive control. The system includes: an environmental perception and terrain classification module, which acquires 3D environmental information through the fusion of stereo vision and LiDAR, and generates a structured environmental description containing traversable areas, obstacle locations, and terrain categories based on a point cloud segmentation algorithm; an online parameter identification and state estimation module, which estimates robot dynamic parameters and outputs the system state using an extended Kalman filter; a task decision and energy management module, which generates motion sequences and configures optimization weights based on a fuzzy inference system; and a multi-objective predictive controller, which constructs a cost function based on a dynamic model and solves for the first element of the control sequence using a sequential quadratic programming algorithm. This invention enables dynamic behavior optimization under complex terrain and variable task requirements, effectively balancing motion performance and energy consumption, and improving the robot's autonomous adaptability and task sustainability.
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