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Single-sensor zero-velocity-update (ZUPT)-aided pedestrian inertial navigation system (PINS) is a conventional method. However, the single-sensor systematic error still causes imprecise estimation of step length and high drift of orientation. In this study, three magnetometers and inertial measurement units (MIMUs) are mounted on single-leg toe, shank, and thigh, respectively. Gaits are divided into two phases: stance and swing. Based on the biomechanical characteristics of a human lower limb, a velocity-position estimation algorithm is proposed. The positions of the three sensors are fused by dynamic geometric relationships, which are formulated through the natural lower-limb model and attitude estimation. A velocity-difference constraint is proposed to suppress the divergence of velocity, and Coriolis-based velocity correction is designed to observe velocities of the toe, shank, and thigh at the stance phase. A MAHONY-linear-Kalman framework separately estimates attitudes, velocities, and positions to reduce calculations. The proposed method is compared with foot-mounted, shank-mounted, and thigh-mounted ZUPT-aided PINS as well as a dual-sensor foot-to-foot algorithm through experiments. In the flat terrain experiment, compared with the errors of the Shank-mounted INS, the Thigh-mounted INS, the Foot-mounted INS, the Foot-to-Foot Constraint Method, the root mean squared error (RMSE) of the proposed method can be reduced by 40.34%, 71.85%, 29.53%, and 7.08%,, respectively. In the slope experiment, compared with the other four methods, the root mean squared error (RMSE) of the proposed method can be reduced by 80%, 86.74%, 67.54%, and 61.86%, respectively. In the natural terrain experiment, compared with the other four methods, the root mean squared error (RMSE) of the proposed method can be reduced by 81.63%, 96.27%, 70%, and 60.87% respectively. The results show that the proposed method greatly suppresses the positioning and orientation errors in different scenes.

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Car rollovers are a class of serious traffic accidents that can easily cause heavy casualties and property damage, particularly for special operation vehicles. To enhance the driving stability of vehicles on forest roads, we developed a control strategy for wire-controlled auxiliary braking based on body-attitude detection and the overall design of the system. Moreover, the control system was further investigated and developed. A three-degrees-of-freedom (3-DOF) vehicle dynamics model with longitudinal, lateral, and lateral tilt was developed based on actual-vehicle test data. The lateral load-transfer rate (LTR) of the vehicle was selected as the early warning algorithm for vehicle rollover; the differential braking of the vehicle was realized by adjusting the pressure of the wheel cylinders; and automatic speed reduction was achieved according to the rollover attitude of the vehicle by combining the fuzzy-PID control algorithm. Finally, a vehicle dynamics model was developed, and the results verified the effectiveness of the anti-rollover control strategy under extreme operating conditions.

Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.