Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem that it is difficult to take into account both obstacle avoidance and formation when multi-AUV system plans path in unknown environment, this paper proposed a multi-AUV cooperative obstacle avoidance method based on leader-follower and behavior. First, this paper designed a local path planning method for AUV by constructing the evaluation function of collision risk and deviation from the target; on this basis, combined with formation control method, this paper designed different behaviors and behavior selection patterns for leader and followers respectively. The semi-physical simulation experiment results show that the algorithm can realize the cooperative obstacle avoidance of multi-AUV system in unknown environment, and the formation deviation and recovery time are better than traditional multi-robot obstacle avoidance algorithms. Experimental results verify the feasibility and effectiveness of the algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： Fuzzy Test has good applicability in the exploitation of vulnerabilities in industrial control protocols. However, the traditional fuzzy test has the disadvantages of large test workload and a high failure rate. In order to solve these problems, it design an industrial control protocol fuzzy tester GA-fuzzer which combines genetic algorithm and fuzzy test. and propose the concepts of dangerous points and case space model based on dimensional transformation. In GA-fuzzer, it constructed a more efficient dynamic fitness function, and design dynamic mutation and crossover operators to optimize test cases. In the same experimental environment, it used open source fuzzy test method Peach and GA-Fuzzer to test the target. The results show that GA-fuzzer can effectively improve the premature convergence problem of traditional genetic algorithm, and compared to Peach, the number of cases used to achieve the same test expectation was reduced by 27.20% and the test time was reduced by 34.82%.
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