Vehicles are developing in the direction of energy-saving and electrification. suspension has been widely developed in the field of vehicles as a key component. Traditional hydraulic energy-supply suspensions dissipate vibration energy as waste heat to suppress vibration. This part of the energy is mainly generated by the vehicle engine. In order to effectively utilize the energy of this part, the energy-regenerative suspension with energy recovery converts the vibrational energy into electrical energy as the vehicle’s energy supply equipment. This article reviews the hydraulically powered suspension of vehicles with energy recovery. The importance of such suspension in vehicle energy recovery is analyzed. The main categories of energy-regenerative suspension are illustrated from different energy recovery methods, and the research status of hydraulic energy-regenerative suspension is comprehensively analyzed. Important factors that affect the shock-absorbing and regenerative characteristics of the suspension system are studied. In addition, some unresolved challenges are also proposed, which provides a reference value for the development of energy-regenerative suspension systems for hybrid new energy vehicles

Due to the divergence of accuracy caused by inertial measurement unit (IMU) cumulative error, it is difficult for a single IMU equipment to realize vehicle positioning. Therefore, this paper proposes an IMU pose state estimation algorithm based on modulation long short-term memory-unscented Kalman filter (ML-UKF) algorithm. First, the algorithm improves the memory mode of LSTM network by using Modulation LSTM neural network and establishes IMU state model and observation model. Then, in order to adapt to the application of deep learning algorithm in UKF, an equal spacing sigma sampling method is proposed. Finally, the effect of IMU pose state estimation is verified by experiments. Results show that the root mean square error of the ML-UKF algorithm is decreases by 65.43% relative to the state of the art, further verifying the effectiveness of the proposed algorithm.

In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function-neural network (RBF-NN) is proposed in this article. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation (DE) value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance (SNMR) signals. Results show that effective weak signals can still be obtained when SNR = −10 dB, verifying the effectiveness of the algorithm.

Precise and robust extrinsic parameter calibration is fundamental for LiDAR-camera multi-modal sensing applications. However, most existing methods assume that sensors have the same orientation, limiting their effectiveness in feature extraction and feature alignment from different angle of view in multi-angle sensing scenarios. Moreover, the calibration accuracy of existing methods is insufficient for high-performance applications. To address these limitations, we propose a novel automatic extrinsic parameter calibration method utilizing a spherical target. We propose the Curvature Consistency Spherical Detection (CCSD) algorithm for LiDAR point cloud sphere recognition. The CCSD leverages the sphere's structural attributes, enabling robust detection against noise and partial occlusion. To improve camera sphere detection, we present an enhanced ellipse detection technique and compensate the eccentricity error arising from spherical projection based on the principle of perspective transformation. Extensive simulations and real-world experiments demonstrate the proposed method's superiority in accuracy and practicality over state-of-the-art (SOTA) methods.

Robotic arms with autonomous operation capabilities are currently widely used in real life, such as fruit picking, cargo handling, and workpiece assembly. However, the common autonomous operation methods of the robotic arm have some disadvantages, such as poor universality, low robustness, and difficult implementation. An autonomous operation method of multi-DOF (Multiple Degree of Freedom) robotic arm is proposed in this study on the basis of binocular vision. First, a target object extraction method based on extracting target feature points is proposed, and combined with the binocular positioning principle to obtain the spatial position of the target object. Second, in order to improve the working efficiency of the robotic arm, the robotic arm motion trajectory is planned on the basis of genetic algorithm in the joint space. Finally, a small physical prototype model is built for experimental verification. The experimental results show that the relative positioning error of the target object can reach 1.19% in the depth of field of 70–200 mm. The average grab error, variance, and grab success rate of the robot arm are 14 mm, 6.5 mm, and 83%, respectively. This shows that the method proposed in this paper has the advantages of high robustness, good versatility and easy implementation.