In this paper, an robot parallel evolution design algorithm is proposed, based on the idea of module network, and the learning method is verified and tested as well. The learning method of collision avoidance, approach and wall switching behavior of evolutionary robots based on neural network method is optimized. The evolutionary robot can autonomously realize the behaviors of collision avoidance, movement, replication and attack. In the simulation environment of this paper, the obstacles in the environment are represented by a randomly generated rectangular region with the largest side length. The shape and location of obstacles are evenly distributed. The simulated environment has an approach target but no obstacles, and these approach targets are placed randomly and scattered in the environment. The formation of behavior is integrated with basic behaviors such as collision avoidance and approach, so that the robot is gradually enhanced based on the growth of neural network.

This paper presents a logical modeling of a multi-process monitoring system that uses a formal model to verify the correctness and efficiency of the system. The system consists of multiple processes that run in parallel and communicate with each other through private channels. The system uses an accelerated kernel interpreter to execute the connection models on different subsets of the multi-process data. The paper describes the main operations and functions of the system, such as allocating, setting up, switching, and running the multi-process connections. The paper also provides several assertions that check the validity of the parameters and the results of the system. The paper demonstrates how the logical modeling of the multi-process monitoring system can improve the reliability and performance of the system.