Aiming at the low classification accuracy of unbalanced datasets, an unbalanced data classification algorithm based on improved SMOTE (Synthetic Minority Oversampling TEchnique) and AdaBoost algorithm (KSMOTE-AdaBoost) was proposed. Firstly, a noise sample identification algorithm was proposed according to the idea of K-Nearest Neighbors (KNN). The noise samples in the sample set were accurately identified and filtered out by the number of heterogeneous samples included in the K neighbors of the sample. Secondly, in the process of oversampling, the sample set was divided into different sub-clusters based on the idea of clustering. According to the cluster center of the sub-cluster and the number of samples the sub-cluster contains, the synthesis of new samples was performed between the samples in the cluster and the cluster center. In the process of sample synthesis, the data imbalance between classes as well as in the class was fully considered, and the samples were corrected in time to ensure the quality of the synthesized samples and balance the sample information. Finally, using the advantage of AdaBoost algorithm, the decision tree was used as the base classifier and the balanced sample set was trained and iterated several times until the termination condition was satisfied, and the final classification model was obtained. The comparative experiments were carried out on 6 KEEL datasets with G-mean and AUC selected as evaluation indicators. The experimental results show that compared with the classical oversampling algorithm SMOTE and ADASYN (ADAptive SYNthetic sampling approach), G-means and AUC have the highest of 3 groups in 4 groups. Compared with the existing unbalanced classification models SMOTE-Boost, CUS (Cluster-based Under-Sampling)-Boost and RUS (Random Under-Sampling)-Boost, among the 6 groups of data:the proposed classification model has higher G-means than CUS-Boost and RUS-Boost, and 3 groups are lower than SMOTE-Boost; AUC is higher than SMOTE-Boost and RUS-Boost, and one group is lower than CUS-Boost. It is verified that the proposed KSMOTE-AdaBoost has better classification effect and the model has higher generalization performance.

There are many redundant points and inflection points in the path planned by the traditional A^* algorithm. Therefore, an efficient path planning algorithm based on A^* algorithm was proposed. Firstly, the specific calculation method of the evaluation function was improved to reduce the calculation amount of the algorithm searching each interval, thereby reducing the path finding time and changing the generation path. Secondly, on the basis of improving the specific calculation method of the evaluation function, the weight ratio of the evaluation function was improved, and the redundant points and inflection points in the generation path were reduced. Finally, the path generation strategy was improved to delete the useless points in the generation path, improving the smoothness of the path. In addition, considering the actual width of the robot, the improved algorithm introduced an obstacle expansion strategy to ensure the feasibility of the planned path. The comparison of the improved A^* algorithm with three algorithms shows that the path of the improved A^* algorithm is more reasonable, the path finding time is shorter, and the smoothness is higher.

In the process of mobile robot path planning, it is difficult for the Rapidly-exploration Random Tree (RRT) algorithm to sample narrow channels. In order to deal with this problem, an improved bridge detection algorithm was proposed, which is dedicated to narrow channel sampling. Firstly, the environment map was pre-processed and the obstacle edge coordinate set was extracted as the sampling space for the bridge detection algorithm, thus avoiding a large number of invalid sampling points and making the sampling points distribution of the narrow channel more rational. Secondly, the process for bridge endpoint construction was improved, and the operation efficiency of the bridge detection algorithm was increased. Finally, a slight variant Connect algorithm was used to expand the narrow channel sample points rapidly. For the narrow channel environment map in the experiment, the improved algorithm has the success rate increased from 68% to 92% compared with the original RRT-Connect algorithm. Experimental results show that the proposed algorithm can sample the narrow channel well and improve the efficiency of path planning.