Scene understanding and intelligent decision-making method for planet probe vehicle in complex unknown environment

Through multi-sensor fusion of environmental perception and intelligent decision-making methods, the planet exploration rover's scenario understanding and decision-making problems in complex and unknown environments are solved, accurate identification and risk assessment of obstacles are achieved, and the autonomous decision-making ability and driving efficiency of the detection rover are improved, ensuring safety and stability.

CN120540283APending Publication Date: 2025-08-26HEFEI UNIV OF TECH
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Patent Information

Application Number
CN202510692499.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-27
Publication Date
2025-08-26

AI Technical Summary

Technical Problem

Planetary rovers face the problems of incomplete understanding of scenarios and unclear decision-making mechanisms in complex and unknown environments. Traditional methods have defects in obstacle risk modeling, mathematical characterization of passable areas and multi-sensor fusion efficiency, which is difficult to meet the needs of safe and efficient driving.

Method used

Multi-sensor fusion environment perception is adopted to obtain multi-dimensional environmental information through space-time alignment processing, build collapse function and potential field function, combine improved clustering algorithms and genetic algorithms to dynamically quantify obstacle risks, generate optimal paths, and monitor and adjust in real time to form a closed-loop control system.

Benefits of technology

It realizes accurate identification and risk assessment of polymorphic obstacles, improves the independent decision-making ability and driving efficiency of the detection vehicle in complex environments, and ensures the safety and stability of the detection vehicle.

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Abstract

The invention discloses a scene understanding and intelligent decision-making method for a planet probe vehicle in a complex unknown environment, and relates to the technical field of deep space exploration and intelligent vehicles, and the method comprises the steps: obtaining multi-dimensional environment information; constructing a slump function based on the geometric morphology, motion characteristics and material attributes of the obstacle, and dynamically quantifying the risk of the obstacle; smoothing the potential field function by adopting Gaussian filtering, and dynamically adjusting the weight of a global potential field and the weight of a local potential field; in combination with an improved clustering algorithm, fuzzy regions of obstacles capable of being crossed and obstacles not capable of being crossed are accurately divided; screening passable areas by constructing a credibility mean square value function; carrying out multi-objective optimization by adopting a genetic algorithm, and generating an optimal path matched with the environment in cooperation with an offline data set and online reasoning; and inputting the optimal path into a bottom layer control system of the probe vehicle, controlling execution of a motor driving and steering mechanism, monitoring the state of the probe vehicle in real time, and triggering dynamic re-planning. And the autonomous obstacle avoidance capability and the path planning efficiency of the probe vehicle in a complex environment are improved.
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