A task assignment method based on deep learning inference for resource-constrained robots

Abstract

The invention discloses a task allocation method based on deep learning reasoning of a resource-limited robot, and aims to realize the task allocation of a multi-robot system to robot engineering tasks. The technical solution is to build a task allocation system, build a layer prediction model and store it on the robot; the task robot collects environmental data, the model interpretation module extracts the model layer type and related parameter configuration, and the resource consumption estimation sub-module estimates the level of each robot based on the layer prediction model. The delay of each layer of the deep learning model; the resource availability sub-module queries the resource status of the task allocation system; the decision-making module determines the optimal task allocation plan, and allocates engineering tasks to the robot nodes according to the optimal task allocation plan; The task allocation plan carries out the execution of engineering tasks. The invention can coordinate engineering task allocation between heterogeneous robots according to the task allocation system resource state, and optimize the execution time of the engineering task.

Description

technical field [0001] The present invention relates to the field of robot distribution and multi-robot task assignment, and specifically relates to the realization of the engineering task assignment of multiple resource-limited robots based on the idea of ​​model division based on the collective processing capability of multi-cooperative resource-limited robots, so that the limited robots Groups can cooperate to complete engineering tasks that traditional single robots cannot complete (ie, engineering tasks that require deep learning). Background technique [0002] A robot is a "physical agent that completes tasks by manipulating the physical world", and has the ability to interact with the physical world similar to human beings, and even surpasses human beings in some aspects. However, just like a single individual, a single robot system has been unable to meet the increasingly complex task requirements of modern society due to its own limitations in perception and process...

AI Technical Summary

Problems solved by technology

3) Unknown capabilities: In a multi-robot system, the capabilities of the robots cannot be clearly known, so more work cannot be assigned to more capable nodes

However, this solution is only for homogeneous robots, and only considers the resource-constrained characteristics of robots, without considering the more computing power and unequal resources faced by heterogeneous robots, and this solution does not consider multi-robots The unknown characteristics of resources in the system are based on the number of isomorphic robots to divide and assign tasks based on deep learning classification tasks

At present, there is no public technical solution related to the deep learning-based engineering task assignment method based on heterogeneous robots

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