The robotic manipulator has become an indispensable piece of the manufacturing equipment. The key to guiding its movement is path planning technology, which is becoming increasingly important due to the complex working environments, in which the robotic manipulator is used. This paper proposes the improved bidirectional rapid exploration random trees algorithm( IBi-RRT ) and applies it to the path planning of a 6 degrees of freedom (6-DOF) robotic manipulator. The proposed algorithm has two improved strategies. Firstly, the idea of preferential selection of random node is adopted to reduce randomness in the expansion. Secondly, the adjustable attractive force size strategy is used to increase search speed. In order to make the planned path more in line with the actual motion path of the 6-DOF robotic manipulator, a quadratic B-spline function is used to smooth the initial path. Simulations and experiments are performed to verify the effectiveness and superiority of the proposed algorithm. Simulation results and experiments demonstrate that the IBi-RRT algorithm can safely move the 6-DOF robotic manipulator to the designated position.

A compound scheme, based on an improved active disturbance rejection controller (ADRC) and nonlinear compensation, is developed for the electromechanical actuator (EMA) system in this paper. First, considering the influences of backlash, friction on the EMA system, a model for the EMA system is presented. The LuGre model and Hysteresis inverse model are used to compensate for the friction and backlash phenomenon. Then, the method of improved ADRC, based on the Fal function filter and a Linear extended state observer (LESO), is investigated. Simultaneously, since the controller parameters of the improved ADRC are complicated, the non-dominated sorting genetic algorithm II (NSGA-II) is presented to optimize the controller parameters, to achieve the best dynamic response. Finally, simulation and experiment are presented to validate the effectiveness of the proposed method. Under the nonlinear compensation, the performance of the proposed compound scheme is compared with the conventional proportional integral (PI) controller, in terms of step response analysis and sine wave response analysis. Simulation and experiments show that the proposed controller provides high-performance dynamic characteristics.

This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet + + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness.