Intelligent Obstacle Avoidance Algorithm for Mobile Robots in Uncertain Environment

The application of mobile robots and artificial intelligence technology has shown great prospects in many fields. ability intelligent obstacle avoidance is the basis for deep robots. However, there are often more or less uncertain factors actual operating environment robot, such as people objects that not updated time temporarily appear. Therefore, it an important step to complete automatic learning In a nondeterministic environment, robot algorithm based on improved fuzzy neural network with self-learning firstly proposed. system constructed through reaction layer, deliberation supervision layer. Through analysis sensor performance, model accuracy, path optimization, simulation, following conclusions drawn. First, training, accuracy rate test set stable at 98%, loss function value also been reduced from original 0.79 0.08, which 10 times smaller. Second, traditional single cannot meet requirements robots, must combine multipurpose technology. Third, this paper encounters following. When obstacles, dominated by straight lines, planning optimal, distance shorter. Fourth, larger N : M, solution space, indicating gradually improves search efficiency greatest extent can handle any form medium large scale task allocation problem.