Extreme-environment-oriented bulk cargo unloading robot autonomous collaborative operation method and system

By using cross-modal fusion technology of cosmos quantum detection and millimeter-wave radar, the internal state of bulk cargo stacks was reconstructed and the unloading potential field was constructed, solving the problem of real-time monitoring and collaborative control of bulk cargo unloading under extreme environments, and realizing efficient and safe autonomous unloading operations.

CN121590939APending Publication Date: 2026-03-03浙江天新智能研究院有限公司 +4
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Patent Information

Application Number
CN202511777827.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-28
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve real-time three-dimensional state field reconstruction and collaborative control within bulk cargo stacks under extreme environments, resulting in distorted perception data and hindering safe and efficient unloading under complex conditions.

Method used

By employing cross-modal fusion of cosmos-based muon detection and FMCW millimeter-wave radar technology, a three-dimensional voxel field is reconstructed to reconstruct the internal density, void, and arch probability of bulk cargo. This field is then combined with the unloading potential field U for multi-robot partitioning and collaborative control. Robust constraints are introduced in extreme environments to achieve adaptive partitioning and unloading.

Benefits of technology

It enables real-time monitoring and autonomous coordinated unloading of the internal structure of bulk cargo stacks in extreme environments, improving operational efficiency and safety, reducing resource interference and path conflicts, and possessing self-learning and control capabilities.

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Abstract

The invention discloses an extreme environment-oriented bulk cargo unloading robot autonomous collaborative operation method and system. According to the method, Uyuan muon detection units are arranged in an unloading area and on a robot body, the three-dimensional density of a bulk cargo pile and an arch bridge probability voxel field are obtained, cross-modal registration fusion is carried out in combination with charge level geometry and motion data collected by an arm-loaded or vehicle-mounted FMCW millimeter wave radar, and bulk cargo internal state tensors F (x, y, z, t) and a gradient field F thereof are constructed. And the system generates a multi-robot partition and operation sequence according to the real-time calculation result of the unloading potential field U, and automatic unloading, arch breaking and conveying operation are achieved. And when the dust, charge or gas concentration exceeds the limit, the low-energy cooperative mode is automatically switched, the state tensor and the potential field are dynamically updated through the operation receipt, and efficient and safe unloading cooperative control under the extreme environment is achieved.
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