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AGV real-time path planning method based on optimal control and width learning

A technology of real-time path planning and optimal control, applied in the direction of non-electric variable control, control/adjustment system, two-dimensional position/channel control, etc., can solve the slow training process, complex adjustment of deep neural network parameters, and low time-consuming and other problems, to achieve the effect of long drawing time, high environmental requirements and low time consumption

Pending Publication Date: 2022-03-18
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of complex adjustment of deep neural network parameters and slow training process in the existing AGV path planning method based on deep learning, the present invention proposes an AGV real-time path planning method based on optimal control and width learning, without artificial Strong prior parameter adjustment work, offline efficient training of width learning network, low time consumption, and further expansion and application for future large-scale workshop AGV formation and obstacle avoidance applications

Method used

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  • AGV real-time path planning method based on optimal control and width learning
  • AGV real-time path planning method based on optimal control and width learning
  • AGV real-time path planning method based on optimal control and width learning

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Embodiment 1

[0067] The path planning problem of AGV can be regarded as a trajectory planning problem in which the starting point of AGV is not fixed, but the end point (that is, the coordinate point of the delivery task) is fixed. The traditional trajectory method can be divided into: path search + trajectory optimization, but the traditional path search The method is often based on a grid map, and the searched route does not necessarily conform to the vehicle dynamics constraints (the vehicle cannot make a lateral movement). Therefore, it brings uncertainty in the optimization time and optimization quality to the later trajectory optimization. Therefore, in actual implementation, in order to ensure the effectiveness of path planning results, it is very necessary to consider the dynamic model of AGV as a constraint in the planning at the beginning stage. The trajectory planning problem with a fixed end point is solved in the forward direction by the optimal control method for solving the t...

Embodiment 2

[0121] In this embodiment, in addition to the training described in Embodiment 1 for the width learning network, since the magnitude of the data set is not within an order of magnitude, the training data set is normalized before being used for the width learning network training Processing, using methods including but not limited to max-min normalization, Z-score normalization and functional transformation.

[0122] The optimal state S and control rate C in the training data set with a certain position as the target state are input into the width learning network after training (for example, starting as ), the output of the width learning network needs to be inversely normalized, and finally used as a control rate that conforms to the physical meaning.

Embodiment 3

[0124]In this embodiment, on the basis of Embodiment 1 and Embodiment 2, focus on the discussion of AGV path planning that expands the initial point to any point and expands the destination to different end states. Under different starting points, the "optimal state-control rate" pair (s, c) trains a width learning network alone, and by combining the pseudo-inverse matrix solution of the incremental method, it can quickly learn the optimal state to the most optimal state. The weight matrix W of the optimal control mapping relationship can solve the optimal control problem when the starting point changes, and extend the initial point in the online optimal control to any point in the set area.

[0125] Among them, the number of feature nodes N in each group of the width learning network, the number of enhanced nodes M, and the number of newly added enhanced nodes can be selected according to the trade-off between computing power and prediction accuracy in specific scenarios.

[...

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Abstract

The invention proposes an AGV real-time path planning method based on optimal control and width learning, and relates to the technical field of AGV path planning, and the method comprises the steps: firstly constructing an AGV dynamical model, taking the AGV dynamical model as a dynamic constraint, taking time-burn-up optimization as a target function, building an optimal control model, and carrying out the offline forward solving, and generating an optimal control trajectory of a plurality of different starting points, under the condition, considering that offline optimization is relatively difficult to realize real-time optimal control so as to achieve the optimal trajectory, in order to avoid hysteresis caused by offline optimization solution, introducing a width learning network, taking different target points as a classification basis, and solving the optimal control trajectory of the plurality of different starting points. And integrating and classifying the optimal control trajectory into a training data set of different navigation tasks, and incrementally training a width learning network to obtain a width learning network finally used for AGV real-time path planning, thereby realizing real-time optimal control of AGV path planning at any starting point within a certain range.

Description

technical field [0001] The present invention relates to the technical field of AGV path planning, and more specifically, relates to an AGV real-time path planning method based on optimal control and width learning. Background technique [0002] Automated Guided Vehicle (AGV for short), refers to a transport vehicle equipped with automatic guidance devices such as electromagnetic or optical, capable of driving along a prescribed guiding path, with safety protection and various transfer functions, and is a flexible production vehicle. The key equipment of the system plays an important role in object handling automation and intelligent warehousing. [0003] At present, the dynamic and flexible manufacturing environment brings many challenges to AGV path planning and real-time control in the workshop. AGV can be roughly divided into three types: remote control type, semi-autonomous type and autonomous type according to its control mode and degree of autonomy. Navigation based o...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221Y02T10/40
Inventor 吴宗泽赖家伦李嘉俊任志刚曾德宇
Owner GUANGDONG UNIV OF TECH
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