To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting point...
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ISBN:
(纸本)9781538639955
To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting points is decomposed into a number of path planning sub-problems with small-scale detecting points by density peaks clustering algorithm(DPC), and then all sub-problems with small-scale detecting points are resolved by Ant Colony Optimization algorithm(ACO). At last, all the paths obtained from each sub-problem are merged, and local path is optimized by 2-Opt and 3-Opt algorithms. The hierarchical algorithm is tested on 5 different instances. Experimental results show that the proposed algorithm has the same accuracy and computational complexity can be decreased n squared compared with ACO algorithm. The efficiency has been significantly improved.
This paper proposed a novel hybrid algorithm for path planning at macroscopic level for autonomous climbing robot. The path planning is an Asymmetric Traveling Salesman Problem (ATSP). The problem can be decomposed in...
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ISBN:
(纸本)9781538695944
This paper proposed a novel hybrid algorithm for path planning at macroscopic level for autonomous climbing robot. The path planning is an Asymmetric Traveling Salesman Problem (ATSP). The problem can be decomposed into some groups with small-scale points by density peaks clustering algorithm (DPC), and these groups are divided into two types based on the node distribution: dense groups and sparse groups. Then local paths for all groups are solved by Ant Colony Optimization algorithm (ACO) and merged at the nearest pair of points between the two adjacent groups to generate the initial global path. At last, the final global path is obtained after optimizing by K-Opt algorithms. The proposed algorithm is tested on nine benchmark instances compared with four other algorithms. Simulation experiment result shows that the proposed algorithm can provide the solution with higher accuracy and shorter runtime.
According to the energy variation of the mechanical transmission in the process of circuit breaker operation which is characterized by acoustic and vibration signals, a new method of high Voltage circuit breaker mecha...
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According to the energy variation of the mechanical transmission in the process of circuit breaker operation which is characterized by acoustic and vibration signals, a new method of high Voltage circuit breaker mechanical fault diagnosis was proposed in this paper. This method combined density peaks clustering algorithm (DPCA) fused Kernel Fuzzy C Means (KFCM) and support vector machine (SVM). It is an intelligent method of double clustering. Vibration and acoustic signals are decomposed by Local Mean Decomposition. Three product function components with the largest correlation of the original signal are filtered. And the characteristic entropy can be extracted by approximate entropy. DPCA is utilized to get the best peak densityclustering decision and optimize the initial clustering center of KFCM. The fault training samples is pre-classified and input SVM. And the fault classification result of the circuit breaker can be received by mesh optimization algorithm. Finally, the DPCA-KFCM and SVM method in the fault diagnosis of the circuit breaker is verified by the typical failure test of the circuit breaker, the loosening of the pedestal and the refusal of the circuit breaker, which improve the accuracy of the fault diagnosis greatly.
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