To achieve automatic rolling of an unmanned vibratory roller, the path following control model of the vibratory roller was built and the influence of drum vibration is considered since the roller always works in the vibration condition. The vibration dynamic model was first established. The dynamic model of the vibratory roller under the influence of exciting force was then developed. The heading following control algorithm based on preview strategy was designed to control the path following errors in unmanned automatic rolling. The control performance of the algorithm was analyzed by simulation based on the vibration dynamic model and the dynamic and kinematic models of the vibratory roller. The control performance was also verified by the field automatic rolling experiments. Experimental results show that the maximum absolute lateral error of line path following is 24.5 cm in vibration rolling and 19.5 cm in non-vibration rolling at the traveling speed of 0.83 m/s. The average absolute lateral errors in vibration and non-vibration rolling are both less than 10 cm. The experimental results verify the effectiveness and achievability of the control algorithm for unmanned vibration rolling. Besides, the ratio of completely compacted area to the whole set area is 97.28% in the field automatic rolling experiment, which meets the requirements for practical use.

Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.

Unmanned road roller has become an attractive solution to reduce operators’ working intensity and improve compaction efficiency. As a result of working in bumpy environments, sideslip effect is unavoidable during the compacting process. In order to guarantee the reliability and stability of path tracking control, a novel and effective method based on preview control and sideslip compensation is proposed in this paper. The kinematic and dynamic models of articulated road roller considering sideslip angles are obtained first. Then, a preview controller with sideslip compensation (PCSC) is developed to eliminate the path tracking errors between the actual path and the desired path. To evaluate the control performance of the proposed method, simulation under the MATLAB/Simulink and automatic compaction experiments at the construction site were performed with initial errors. Finally, the simulation and experimental results show that the path tracking errors can converge to zero as time goes by. The proposed method has the ability to navigate an articulated road roller to track a desired path in the existence of sideslip, only regarding the steering angle dynamic as control input. Compared with other benchmark method, it not only improves the control accuracy, but also increases the convergence rate, proving its effectiveness and feasibility. All these also demonstrate the practical value and validity of the proposed models with sideslip.

The hydraulic joint is the key driving component of a robot. To reduce the joint size of the hydraulic robot, and improve the control accuracy and dynamic response performance, this paper proposes a novel joint structure and control method of a ball double-screw hydraulic robot. Using ball and circular arc spiral groove transmission, the hydraulic joint has a small transmission friction coefficient, compact overall structure and higher transmission accuracy. Aiming to resolve the problems of low control accuracy and motion instability caused by temperature drift in valve-controlled hydraulic systems, the high-precision joint control method based on adaptive fuzzy control compensation is used to improve the control accuracy and stability. The static and dynamic characteristics of the designed hydraulic joint are analyzed by simulation. A test platform was built, and the physical prototype of the hydraulic joint underwent static testing, dynamic control, amplitude frequency response and trajectory tracking tests. The experimental results were similar to the simulation results. The ball double-screw hydraulic robot joint has the characteristics of low starting pressure, high energy density, fast dynamic response, small amplitude frequency attenuation and high control accuracy. The starting pressure is 0.5 MPa, maximum swing frequency is 3 Hz, positioning accuracy is ± 0.03°, tracking accuracy is ± 3.9° and maximum angular velocity at 10 MPa is about 7.6 rad/s, which is close to the angular velocity of the actual human joint.