Body-aware multitask planning method and device, electronic equipment and storage medium

By generating parallel-executable task combinations through instance database parsing and resource status detection, and combining real-time sensing data for action instruction planning, the problem of resource contention and spatial conflict in multi-task execution of embodied sensing devices is solved, achieving efficient and stable multi-task execution.

CN122450673APending Publication Date: 2026-07-24深圳若愚科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
深圳若愚科技有限公司
Filing Date
2026-05-08
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing multi-task execution planning methods for embodied sensing devices lack systematic joint judgment, which may lead to resource contention, state mutual exclusion, and execution space conflicts between tasks, affecting execution efficiency and stability.

Method used

The task instructions are instantiated, parsed, and prioritized using an instance database. Conflict detection is performed in conjunction with hardware and environmental resource status to generate a combination of tasks that can be executed in parallel. Real-time perception data and execution feedback are introduced to plan action instructions and form a multi-threaded action sequence.

Benefits of technology

It improves the reliability and overall execution efficiency of multi-task planning, enhances the adaptability to complex scene changes, avoids task conflicts and resource interference, and improves the coordinated utilization of equipment and environmental resources.

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Abstract

Embodiments of the present application provide a body-aware multi-task planning method and device, electronic equipment and storage medium, which are used in the technical field of task execution planning. The body-aware multi-task planning method comprises: obtaining a task queue by performing instantiation analysis and priority sorting on a task instruction according to an instance database; obtaining a task group queue that can be executed in parallel by performing conflict detection of resources and states and combination sorting of parallel execution on each task in the task queue according to real-time sensed hardware resource states and environment resource states; and obtaining a multi-thread action instruction sequence by performing action instruction planning on each task group in the task group queue according to the sensing data detected by each body intelligent device in real time and matched with the corresponding execution unit. The present application realizes safe parallel and stable and efficient execution of multiple tasks by multi-modal instruction analysis, task priority sorting, resource and space conflict detection, and dynamic re-planning combined with real-time sensing feedback.
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