Nao robot path planning method based on deep Double-Q network

Abstract

The invention relates to a Nao robot path planning method based on a deep Double-Q network, which aims at an uncertain environment, utilizes local information acquired by a robot to realize active obstacle avoidance in an indoor environment, plans a path to reach a set target point, and improves the generalization ability of a common path planning algorithm. Through preprocessing training environment data to approach local environment information which can be acquired by a robot in a real scene and setting a proper state space, a proper action space and a proper reward function, the method can directly utilize the acquired local environment information to perform effective obstacle avoidance and path planning in an unknown environment; the method overcomes the defect that a traditional algorithm needs to obtain sufficient environmental information and perform modeling again to explore the environment to a certain extent, improves the generalization ability of the algorithm, and is beneficial to solving the problem of path planning in an unknown environment.

Description

technical field [0001] The invention belongs to the field of deep reinforcement learning and path planning, and relates to a Nao robot path planning method based on a deep Double-Q network. Background technique [0002] Path planning refers to the technology of finding a collision-free path from the initial state (including position and attitude) to the target state (including position and attitude) in an environment with obstacles according to certain evaluation criteria. [0003] At present, path planning algorithms can be roughly divided into: classical algorithms and artificial intelligence algorithms. Traditional path planning algorithms mainly include: simulated annealing algorithm, artificial potential field algorithm, tabu search algorithm, etc. With the rise of artificial intelligence, because of its certain self-learning, self-renewal and memory capabilities, many path planning algorithms based on artificial intelligence have been proposed, typical ones are: ant c...

AI Technical Summary

Problems solved by technology

Most of the above algorithms need to have a full understanding of the surrounding environment information of the robot, and then carry out navigation planning based on certain rules. The actual application environment changes a lot and...

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