The application of mobile robots and artificial intelligence technology has shown great application prospects in many fields. The ability of intelligent obstacle avoidance is the basis for the deep application of mobile robots. However, there are often more or less uncertain factors in the actual operating environment of the robot, such as people or objects that are not updated in time or temporarily appear. Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots. In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. The mobile robot intelligent obstacle avoidance system is constructed through the reaction layer, the deliberation layer, and the supervision layer. Through the analysis of sensor performance, model accuracy, path obstacle avoidance optimization, and obstacle avoidance simulation, the following conclusions are drawn. First, through network training, the accuracy rate of the test set is stable at 98%, and the loss of the function value has also been reduced from the original 0.79 to 0.08, which is 10 times smaller. Second, the traditional single sensor cannot meet the obstacle avoidance requirements of robots, and mobile robots must combine multipurpose technology. Third, the algorithm in this paper encounters the following. When there are obstacles, the path is dominated by straight lines, obstacle avoidance planning is optimal, and the distance is shorter. Fourth, the larger N : M, the larger the solution space, indicating that this algorithm gradually improves the search efficiency to the greatest extent and can handle any form of medium and large scale task allocation problem.
The globalization process and acceptance of a new paradigm for world development aimed at attaining sustainable development require extending the concept of good governance, which allows the improvement of institutional quality. At the same time, governance performance depends on corruption and public participation in national audits. The paper aimed to analyze the impact of public participation in national audits on corruption governance. The object of investigation was 30 Chinese provinces for 2008–2017. The study applied a regression model with the preventive, exposed, and defensive functions of audits. The findings showed that the “governance view” of the audit is the root cause of the audit. In addition, the “immune system” function of national audits has an inhibitory effect on corruption, among which the deterrent effect of the defense function is the most-influential. The influence of public participation in national audits on corruption governance is mainly reflected in the deterrence and resistance function of national audits. The paper contributes to audit theory by incorporating the public participation mechanism, improving the governance path of “national audit—public participation—corruption governance”. It allows improving the national audit “immune system” function by considering the democratic connotation of decision-making and developing a good governance environment.
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