Research On Terrain Preview Method Of Active Suspension Control Of Emergency Rescue Vehicle

China is a country with frequent natural disasters and accidents.Passive suspension is used by most of existing emergency rescue vehicles,which has low driving speed and poor ride comfort.Therefore it is difficult to meet the requirements of rapid response of emergency rescue vehicles.Active suspension could make the driving maneuverability higher than passive suspension.But the effect is limited due to signal acquisition?actuator response and so on.If the terrain preview can be get in advance,the suspension system can be controlled in advance,so as to improve the ride comfort of the vehicle.Combined with the national key research and development plan project "Research on Key Technologies of special chassis and suspension for high mobility emergency rescue vehicles(including fire vehicles)"(Project No.: 2016yfc0802902).To improve the vehicle's driving mobility and solve the low accuracy and cumulative positioning error,terrain preview method of active suspension is studied systematically and deeply.(1)To solve the problem of the poor accuracy of terrain preview caused by sensor measurement error,this paper proposes a real-time terrain mapping method base on sensor error.The Gaussian noise is used to make the model of the lidar measurement error.Then,the Gaussian random transformation uncertain is modeled.And the error model of measurement of sensor is integrated into terrain model.The Kalman filter is used to fuse the terrain measurement values and terrain estimated values.According to the experimental results,the accuracy of the algorithm is 1.7cm.(2)In order to solve the vehicle motion cumulative error caused by vehicle horizontal motion,a real-time mapping and map fusion algorithm based on dimension reduction and motion error transmission is proposed.According to the uncertainty of coordinate system rotation in the process of motion,map dimension reduction and terrain center coordinate real-time alignment strategy are proposed.And the horizontal motion error of vehicle motion is estimated with high accuracy.To improve the problem that the measurement errors at different times at the same point,an error estimation method of inertial navigation system based on random walk model is proposed.According to the theory of confidence ellipse,the map fusion strategy based on weight allocation is designed.The experimental results show that the algorithm effectively reduces the influence of horizontal motion error on terrain accuracy,and the horizontal mapping accuracy is improved by 21.4% compared with the existing LEGO-LOAM algorithm.(3)In order to solve the problems of low positioning accuracy and low data update frequency of front-end laser odometer,the multi-source data fusion strategy of improved error state Kalman filter algorithm based on terrain scene is proposed.Firstly,in order to solve the problem of the low estimation accuracy of vehicle odometer using encoder,the characteristics of emergency rescue vehicle motion are analyzed,and a three-axis heavy vehicle odometer model based on kinematics and dynamics is established as the observation value of the algorithm.According to the terrain scene,the weight allocation strategy of multi-source information fusion is proposed.By integrating the high-frequency IMU data combination with observation,the purpose of improving the data update frequency and accuracy is realized.The experimental results show that compared with the existing NDT algorithm,the improved algorithm proposed in this paper obtains higher pose output frequency and accuracy.(4)To solve the problems of the cumulative error in the large scene map positioning,a cumulative error positioning compensation algorithm based on multi-source information fusion is proposed.Analyzing the characteristics of IMU error accumulation,an IMU error estimation method based on back-end optimization is proposed.By adding the laser odometer factor,IMU pre-integration factor and Closedloop Detection factor into the factor graph according to the designed weight for optimization,the accurate estimation of IMU error is realized.On this basis,the inverse compensation strategy for each factor in the factor graph is designed to compensate the pose error of the nodes in the back-end optimization caused by IMU error.The experimental results show that the single point positioning accuracy of the optimization algorithm is about 4cm,and the positioning accuracy is improved by 20.7% compared with LIOM algorithm.