The positioning accuracy has a significant impact on the application of the robotic arm. In this study, a novel method based on improved manta ray foraging optimization algorithm is presented for kinematic parameters calibration, which can reduce the absolute positioning error of the manipulator. The error model of the robot is established according to the Denavit-Hartenberg (D-H) method. The redundancy of the model is analyzed, and the redundant parameters are eliminated. The kinematic parameter identification of the robot is transformed into a nonlinear optimization problem. The original manta ray foraging optimization algorithm is improved by using the natural selection strategy and adaptive parameter control strategy to improve searching performance and convergence speed. The calibration experiment of the self-designed 6-DOF robot arm is carried out. A measurement method for measuring the position of the robot end using a 3D scanner is introduced. The improved manta ray foraging optimization algorithm (IMRFO) is used to identify the kinematic parameter errors of the robot. The experimental results show that by using the improved manta ray foraging optimization algorithm to calibrate the parameter error, the position error of the robot end is reduced from 10.2357 to 0.5483mm, or by 94.6%. The results show that the IMRFO algorithm proposed in this paper has high identification accuracy and fast convergence speed, which can effectively identify the kinematic parameter error of the robot.

In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize control of an omni-directional service robot in path tracking. The weight of the cost function in a traditional MPC needs to be manually adjusted, and it is difficult to adjust to a satisfactory value. In order to improve the performance and the control accuracy of MPC, a fuzzy system trained by ANFIS is used to adaptively adjust the weight of MPC’s cost function to reduce the error in the process of path tracking. The different simulation experiments are conducted to verify the performance of the proposed algorithm. The experimental results show that the distance error of MPC based on ANFIS is reduced by more than 50% under different paths compared with a traditional MPC, and the angle error is reduced by more than 70%. Meanwhile, the stability is increased by around 60%. The results show the feasibility and superiority of MPC based on ANFIS.

In fields such as robotics and autonomous driving, multi-lidar data fusion is often employed to obtain a comprehensive view of the environment, thereby enhancing environmental perception capabilities. Achieving robust multi-lidar fusion relies on precise calibration between sensors. This paper proposes a dual-lidar extrinsic calibration method based on semantic segmentation and tetrahedral structures. The method is realized by a calibration board placed within the common field of view of the lidars while simultaneously capturing static point cloud data from each lidar, without the need for additional sensors or well-initialized extrinsic parameters. Ground removal is then performed using point cloud processing algorithms. Subsequently, the MA-PointMLP semantic segmentation network is introduced to extract the segmented calibration plate, forming a three-sided point cloud together with the ground. Finally, the lidar extrinsic parameters are solved through nonlinear optimization based on feature point pairs from the calibration board. A tetrahedral structure composed of a triangular plate and the ground was constructed for experimental validation. Experimental results indicate that the average absolute error of the proposed method is 0.029 m, with a root mean square error of 0.031 m. The proposed method outperforms the existing calibration methods, providing a research foundation for subsequent multi-lidar data fusion.

With the rapid advancement of artificial intelligence, the four-wheel independent steering and four-wheel independent drive mobile robots (4WIS-4WID) have attracted increasing attention due to their superior maneuverability. However, trajectory planning for such robots remains challenging because the over-constrained nature of the system, where the number of control variables exceeds the system’s degrees of freedom (DoF), leading to a highly coupled and nonlinear control problem. To address this issue, this paper proposes an iterative trajectory planning algorithm. First, the kinematic steering singularity and discontinuity are analyzed, and the kinematic model is reconstructed into a diagonally arranged dual-steering-wheel configuration. Based on this model, the trajectory planning problem is formulated as an optimal control problem (OCP) that incorporates kinematic constraints, boundary conditions, and collision-avoidance constraints through a two-circle geometric approximation and safe corridor method. Furthermore, an iterative optimization framework with a penalty relaxation strategy is developed to enhance numerical stability and ensure convergence to feasible solutions. Experimental results show that the proposed method improves computational efficiency and reduces steering wheel accelerations and traveling wheel jerks. These results indicate the potential of the proposed approach for efficient and smooth trajectory planning in complex and highly maneuverable 4WIS-4WID robotic applications.

LiDAR-inertial odometry (LIO) is a key technique for autonomous navigation of unmanned ground vehicles (UGVs), but LiDAR’s limited vertical field of view (FoV) causes significant vertical pose drift. Existing methods introduce ground observation constraints to mitigate this drift phenomenon, but these are often unreliable. To address this issue, this article proposes a low-drift LIO method that works without ground constraints. It leverages point cloud position uncertainty to build reliable horizontal and vertical matching features separately, compensating for the vertical FoV limitation. Validated via experiments on diverse datasets, the method significantly reduces pose drift and maintains reliable performance.