Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.

A multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm is proposed in this paper to overcome the drawbacks of poor accuracy, low efficiency, and instability of the existing algorithms in the inverse kinematics(IK) solution of the manipulator. In designing the multi-objective function, the proposed algorithm considers the factors such as position, posture, and joint. To improve PSO, the proposed algorithm comprehensively analyzes all factors affecting the global and local searching abilities. Based on this, the initial population is designed following the localized uniform distribution method. Meanwhile, the inertia weight, asynchronous learning factor, and time factor are respectively designed by introducing the iteration factor. Finally, this paper tests the performance of MOFOPSO with three typical functions to obtain a better inverse kinematics solution of the 6-DOF manipulator. Also, six other algorithms are taken for performance comparison. The experimental results indicate that the proposed method not only ensures the stability of the manipulator but also achieves high accuracy and efficiency in solving the inverse kinematics of the 6-DOF manipulator.

The advancement of smartphones has been a prerequisite for multi-sensor fusion-based indoor mapping due to their high penetration and low cost. This paper constructs a sparse single-track semantic map by fusing waypoints, semantic landmarks, and Wi-Fi landmarks, which are scene-representable and navigational in unknown indoor environments. The pedestrian dead reckoning (PDR)-aided visual-inertial simultaneous localization and mapping (PDR-aided VI-SLAM) method uses the PDR velocity as an external observation to constrain the pre-integration of inertial measurements. The semantic objects detected by the YOLO V4 are pre-filtered by the proposed semantic object filtering algorithm before semantics matching. The centroid of feature points clustered by the random-sampling spatial clustering algorithm (R-DBSCAN) characterizes the location of a semantic landmark relative to the waypoints estimated by the PDR-aided VI-SLAM. To enhance the stability of Wi-Fi landmarks, Wi-Fi signals are processed by the proposed sliding window-based Wi-Fi fusion algorithm. By using waypoints as an intermediate quantity, the association relationships between waypoints, semantic landmarks, and Wi-Fi landmarks are established. After that, a lightweight single-track semantic map is constructed. A landmark matching-based localization method is proposed to evaluate the similarity between the local map and the constructed venue map to infer the location of a pedestrian in the venue map. Experiments are conducted in the office building and mall scenes under various illumination conditions and semantic density. Results demonstrate the high quality of the single-track semantic map and high-precision localization of a local map in the constructed venue map.

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.