Addressing the shortcomings of unmanned path planning, such as significant error and low precision, a path-planning algorithm based on the whale optimization algorithm (WOA)-optimized double-blinking restricted Boltzmann machine-back propagation (RBM-BP) deep neural network model is proposed. The model consists mainly of two twin RBMs and one BP neural network. One twin RBM is used for feature extraction of the unmanned path location, and the other RBM is used for the path similarity calculation. The model uses the WOA algorithm to optimize parameters, which reduces the number of training sessions, shortens the training time, and reduces the training errors of the neural network. In the MATLAB simulation experiment, the proposed algorithm is superior to several other neural network algorithms in terms of training errors. The comparison of the optimal path under the simulation of complex road conditions shows the superior performance of this algorithm. To further test the performance algorithm introduced in this paper, a flower bed, computer room and other actual scenarios were chosen to conduct path-planning experiments for unmanned paths. The results show that the proposed algorithm has obvious advantages in path selection, reducing the running time and improving the running efficiency. Therefore, it has definitive practical value in unmanned driving.

In recent years, driverless driving has been the focus of research in the automotive industry in various countries [

For these problems, the paper has proposed a double twin RBP-BP deep learning neural network model based on WOA optimization. In this algorithm, a twin RBP model is used to process the feature map of the unmanned driving path, and another twin RBM model is used to process the path similarity. The WOA algorithm is used to optimize the twin RBM parameters in the deep learning network so that the deep learning neural network training error and the optimal parameters are obtained, which offers better prediction effects.

The restricted Boltzmann machine [

In

In

where Z is the partition function and its expression is:

where

For data expectation, since there is no direct connection between RBM hidden layer units, an unbiased sample of data distribution can be quickly obtained. Suppose randomly given training images are

For the same reason, the probability of the binary state of the visible layer unit is set to 1:

In summary, the main objective of RBM is to calculate

In order to obtain the optimal value of

For the calculation of

In

The whale optimization algorithm [

(1) Surround predator

In the initial stage of the algorithm, humpback whales do not know where the food is; they all obtain the position information of the food through group cooperation. Other whale individuals will approach this position and gradually surround the food, so the following mathematical model is used:

In the

In the

(2) Bubble attack

In this stage, the humpback whale was used to attack the bubble, and the behavior of the whale preying and spitting out the bubble was designed by shrinking the surroundings and spirally updating the position to achieve the goal of local whale optimization.

1) Shrinking and surrounding principle

When

2) Spiral update position

The individual humpback whale first calculates the distance from the current optimal whale and then swims in a spiral. When searching for food, the mathematical model of the spiral is:

In the

3) Food searching stage

Humpback whales obtain good results by controlling the

In the

Because RBM parameter design is an extremely complicated task, RBM has no rules to follow during design, and it is difficult to ensure the optimization of the network. The parameter learning rate in the double-twin RBM-BP deep learning neural networks, the number of visible layers v, the number of hidden layers h, and the parameter set jointly determine the performance of the double-twin RBM-BP neural networks. Therefore, the parameter design complexity is more complicated than the traditional single RBM. Optimizing these six parameters reasonably is the key to the effectiveness of the model in this paper. Using genetic algorithms, particle swarm optimization and other biomimetic algorithms to optimize BP neural network parameters can improve its performance. Thus, this paper uses the more powerful WOA algorithm for parameter optimization of the twin RBM-BP deep learning neural networks. The parameters v, h, and a total of 12 sets of parameters in the twin RBM-BP are optimized by the WOA algorithm.

The RBM-BP combines the advantages of RBM and BP and targets complex and high-dimensional network traffic data. We use RBM’s strong feature learning ability and unsupervised learning of high-dimensional data to remove redundant features and reduce data complexity. This can reduce the training complexity of the data and improve the recognition accuracy of the deep learning network. However, the RBM-BP networks require huge calculations and have shortcomings regarding the training sample library. In this paper, we will use double twin RBM-BP networks to achieve the use of a small number of layers to reduce the number of training sessions. The structure of twin RBM-BP is shown in

During the predation phase,

In the spiral phase,

Through optimization, the parameters _{1,opt}, _{1,opt}, _{1,opt} and _{1,opt} and _{2,opt}, _{2,opt}, _{2,opt} and _{2,opt} can be obtained. Apply the value of the two groups of the optimal parameters

After optimizing the RBM-BP deep learning neural network parameters through the WOA algorithm, the following two sets of RBM models and their corresponding learning rules can be obtained:

According to the process described above, the design process of the algorithm in this paper is shown in

Input: RBM-BP related parameters, whale algorithm related parameters, route start and end points, number of iterations, map data matrix |

To further illustrate the route planning effect of the algorithm in this paper, the basic RBM-BP deep neural networks, CNN networks, Q-Learning network and the improved Q-Learning network are compared with the algorithm in this paper (as shown in

Parameter | Algorithm |
RBM-BP algorithm | CNN | Q-learning | Advance |
---|---|---|---|---|---|

Number of visible layers v | [3,3] | 4 | 4 | 4 | 4 |

Number of hidden layers h | [3,3] | 4 | 4 | 4 | 4 |

Learning rate |
[0.1330,0.1453] | 0.1 | 0.1 | 0.1 | 0.1 |

A/W:0 matrix |
Random initial value | Random initial value | Random initial value | Random initial value |

To verify the advantages of this algorithm in optimizing the parameters of double-stacked RBM-BP neural networks, this paper optimizes and contrasts the double-stacked RBM-BP neural networks using the genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and WOA algorithm. The source of the dataset is UCI wine quality [_{n} is the predicted value, and _{n} is the actual value. The Eq is as follows:

To further illustrate the effect of the algorithm in the real environment in this paper, the flower bed and the machine room were selected as comparative scenes, as shown in

It is found from

In view of the problems of large error and long running time in the path planning of unmanned driving, the RBM-BP deep neural network algorithm based on double superposition is proposed. The algorithm calculates the characteristics and similarity of the path map through two superimposed RBMs. Then, the output feature vectors in the hidden layers of the two superimposed RBM networks are used as input feature vectors of BP neural networks for training and learning again. Finally, the whale algorithm is used to optimize multiple parameters, such as the number of visible layers, hidden layers and the learning rate, which reduces the training time and training errors. Simulation experiments show that the algorithm has a good path-planning result.