自主服务机器人多传感信息改进AES加密仿真

AES Encryption Simulation for Improving Multi-Sensor Information of Autonomous Service Robots

针对信息在传输过程中可能被篡改和破解问题,提出自主服务智能机器人多传感信息加密方法。通过Jaccard系数和相对匹配度衡量自主服务智能机器人传感信息相似度,清洗重复信息,同时利用马氏距离滤除异常传感信息。采用RBF神经网络融合预处理后的多传感信息,分别改进AES加密算法和Henon映射,并将两者相结合构建联合加密算法用于自主服务智能机器人多传感信息加密。仿真结果表明,所提方法加密后字符型数据信息熵更接近于8,图像信息像素变化率更高,加解密时间更短,充分验证了其信息加密效果。

Currently,information may be tampered with or cracked during transmission.Therefore,a multi-sensor information encryption method for autonomous service robots was proposed.Firstly,the Jaccard coefficient and relative matching degree were employed to measure the similarity of sensor information between autonomous service robots,thus cleaning up the duplicate information.Meanwhile,Mahalanobis distance was used to filter out abnormal sensor information.Subsequently,the RBF neural network was applied to fuse the preprocessed multi-sensor information.Moreover,the AES encryption algorithm and Henon mapping were improved separately and combined.Finally,a joint encryption algorithm was used to encrypt multi-sensor information for autonomous service robots.Simulation results demonstrate that after encryption,the information entropy of character data is closer to 8,and the pixel change rate of image information is higher.Meanwhile,the encryption and decryption time is shortened.All the above fully verifies the information encryption effect of the proposed method.