Dynamic Measurement Unit Research Articles

Navigational Context Recognition for an Autonomous Robot in a Simulated Tree Plantation

A sensor fusion technique was developed for estimating the navigational posture of a skid-steered mobile robot in a simulated tree plantation nursery. Real-time kinematic GPS (RTK-GPS) and dynamic measurement unit (DMU) sensors were used to determine the position and orientation of the robot, while a laser range finder was used to locate the tree positions within a selected range. The RTK-GPS error was modeled by a second-order autoregressive model, and error states were incorporated into extended Kalman filter (EKF) design. Through EKF filtering, the mean and standard deviation of error in the easting direction decreased from 4.05 to 2.21 cm and from 8.27 to 1.89 cm, respectively, while in the northing direction, they decreased from 4.64 to 1.81 cm and from 11 to 2.16 cm, respectively. The geo-referenced tree positions along the navigational paths were also recovered by using a K-means clustering algorithm, achieving an average error of tree position estimates of 4.4 cm. The developed sensor fusion algorithm was proven to be capable of recognizing and reconstructing the navigational environment of a simulated tree plantation, which offers a great potential in improving the applicability of an autonomous robot to operate in nursery tree plantations for operations such as intra-row mechanical weeding.

H∞ Sub-optimal Filter for Low-cost Integrated Navigation System

The integrated navigation system, specially the system based on GPS and INS, is a leading trend of navigation technology, and some of typical integrated navigation systems have been developed and tested in the past few years. On the other hand, because INS is very expensive in general, the application of GPS/INS system is also limited in some application fields. In this paper, a low-cost dynamic measurement unit (DMU) and GPS are integrated by using H∞ filter. At first, based on the mathematic description of H∞ filtering problem, a sub-optimal H∞ filter is discussed. Meanwhile, the relationship between H∞ filter and standard Kalman filter is studied. Aiming at the detail position/velocity integrated mode, the application of H∞ filter on low cost integrated navigation is researched through some simulations, including the performance of accuracy, robustness and stability.

Measurements of the horizontal sprayer boom movements by sensor data fusion

A study investigated the horizontal movements of trailed sprayer booms with the aim of distinguishing their yaw and jolt motions as well as their deformations. Two vehicles were equipped with a radar speed sensor and a three-axis dynamic measurement unit, while each boom was instrumented with ultrasonic sensors and accelerometers. Sensor fusion was used to extract yaw, jolt and deformation speeds by assuming that the motion of any point of the boom is equal to the superimposition of complementary high and low-frequency motions, delimited by a cut-off frequency, estimated on the basis of laboratory trials. Tests performed on trailed sprayers equipped with 22 and 24 m boom lengths, on different soils, indicated that yaw, jolting and deformation, respectively, occurred near 0.3, 2 and 1 Hz. Velocity variations relative to the forward speed of the vehicle were lower than 0.57 m/s and were reached at a speed of 3.5 m/s (12.7 km/h). Ground deposit, roughly estimated as proportional to the inverse of the speed, exhibited coefficients of variation between 4 and 6% for all tests.