A multi-objective path planning method for the wave glider in the complex marine environment

We propose a motion planning method for automated vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic information and making a preliminary driving decision. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing three dimensions (lateral distance, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Simulated Annealing Algorithm (SAA) to improve the process of searching for the optimal trajectories. Experiments in three different simulated driving scenarios (speed maintaining, lane changing, and car following) show that the proposed method can efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete space, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval.

https://www.mdpi.com/2073-8994/14/2/208/pdf?version=1642757321

A Motion Planning Method for Automated Vehicles in Dynamic Traffic Scenarios

A review of recent trend in motion planning of industrial robots

A reasonable obstacle avoidance method for AUV 3D path planning is difficult. Existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, this study designs an obstacle collision prediction model (CPM). Based on the results of the simulation operation of the obstacle inertial motion, the safety of the AUV navigation is evaluated to improve the sensitivity of the model to dynamic obstacles. Then, the learning ability of the sequence sample data is enhanced by combining it with the long short-term memory (LSTM) network, and the training efficiency and effect of the algorithm are improved. The trained proximal policy optimization (PPO) network can output reasonable actions to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments, and successfully realizes dynamic obstacle avoidance of AUV in different three-dimensional unknown environments, providing theoretical and technical support for real path planning. 

/pdf/auv-dynamic-obstacle-avoidance-method-based-on-improved-ppo-1n0og4jw.pdf

AUV Dynamic Obstacle Avoidance Method Based on Improved PPO Algorithm

Abstract In this paper, a method for identifying and decoupling geometric errors of rotation axes using vision measurement is proposed. Based on screw theory and exponential product formula, identification equations of position-dependent geometric errors (PDGEs) and position-independent geometric errors (PIGEs) of the rotation axes are established. The mapping relationships between the error twist and geometric errors are established. The error model provides the coupling mechanism of PDGEs and PIGEs. Furthermore, a progressive decoupling method is proposed to separate PDGEs and PIGEs without additional assumptions. The pose parameters, required for solving the identification equations, are obtained by visual measurement. Then, the error terms of PIGEs and PDGEs are determined. Lastly, the error calibration of the rotation axes is investigated, thus providing an average rotary table orientation error reduction of 28.1% compared to the situation before calibration. 

Vision-based measurement for decoupling identification of geometric errors of rotating axes for five-axis platform

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

In order to solve the problem of high measurement cost and complex operation of position-independent geometric errors (PIGEs) calibration on a five-axis motion platform, this paper first proposes a low-cost pose measurement method, based on monocular vision, which can accurately determine the pose in the environment, even with image shadow and noise. Next, an improved method, combining pose measurement and kinematic parameters identification, is proposed to calibrate a five-axis motion platform. The kinematic error model of the platform and the pose planning of automatic image acquisition are established, providing the pose data and motor position data, required for calibration. Combined with the kinematic loop method, the kinematic parameters of the five-axis motion platform are identified, while the geometric structure parameters are accurately calibrated. Before and after calibration, a circular trajectory of the target coordinate system (TCS) origin, relative to the camera coordinate system (CCS), is used to test the comprehensive accuracy evolution of the five-axis motion platform, by comparing the position and orientation errors of the theoretical circle trajectory to the actual one. The experimental data show that, before and after calibration, the average position error of the five-axis motion platform is reduced by 79.46%, while the average direction error is reduced by 86.53%. The experimental results clearly demonstrate that the proposed calibration method significantly improves the comprehensive motion accuracy of the five-axis motion platform, and they verify the practical value and effectiveness of the calibration scheme.

Calibration of five-axis motion platform based on monocular vision

Identification of the geometric errors of rotary axes is important for deciding the machining accuracy of five-axis platforms. In this paper, a programmable identification method is proposed to decouple the position-dependent and position-independent geometric errors (PDGEs and PIGEs) of the rotation axis. First, the kinematics model of the five-axis platform is developed by dual quaternions theory, and then the physical significance of left multiplication and right multiplication of PDGEs error model is analyzed. The motion difference between the base coordinate system and its local coordinate system is compared by simulation analysis. The geometric relationship between rotation direction and motion elements is established based on the error model, which is used to design an optimization algorithm for identifying the orientation errors of PIGEs. Additionally, the effect of linear axis PIGEs is eliminated by equivalent replacement, and a sample regression model is developed to identify the offset errors of PIGEs. The PDGEs are separated considering the Motion First principle. Finally, the proposed model and identification method were verified on a five-axis dispensing platform. 

Dual quaternion-based kinematic modeling for decoupling identification of geometric errors of rotary axes in five-axis platforms


 Appropriate Footprint of Uncertainties (FOUs) are beneficial to the performance of Interval Type-2 (IT2) fuzzy controller, revealing the effect of FOUs is a key problem. In our published work, as the FOUs increase, the IT2 Mamdani and TS fuzzy controllers, using KM or EKM type-reducer (TR), approach the constant and piecewise linear controllers, respectively, while they finally become constant and piecewise linear controllers. To verify the validation of the above results, when a different TR is used, in this study, the effects of other popular TRs (i.e., Nie-Tan, Wu-Mendel, Iterative Algorithm with Stop Condition) on output of IT2 Mamdani fuzzy controller, are explored. We proven that, (1) as the FOUs increase, irrespectively of the TRs used, the IT2 Mamdani fuzzy controllers approach constant controllers, (2) when all the FOUs are equal to 1 (i.e., at their maximum ), the fuzzy controllers using Nie-Tan and Iterative Algorithm with Stop Condition TR become constant controllers. The FOUs of the controllers using Wu-Mendel TR can be infinitely approaching 1 and cannot be equal to 1 (otherwise, the denominator of the TR output expression are equal to 0), hence when FOUs are infinitely approaching 1, the controller will approach the constant controller infinitely. These results imply regardless of which popular TR is used, the IT2 Mamdani fuzzy controller, when using larger FOUs, the fluctuation of the input variables have a limited impact on the output, the ability to deal with system uncertainties will deteriorate. Laboratory control experiments are provided to demonstrate these findings.

/pdf/analysis-of-footprint-of-uncertainty-of-the-it2-mamdani-4983kr1ril.pdf

Xia Ju

Papers

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Calibration of five-axis motion platform based on monocular vision

Dual quaternion-based kinematic modeling for decoupling identification of geometric errors of rotary axes in five-axis platforms

Analysis of Footprint of Uncertainty of the IT2 Mamdani Controllers Using Different Type-Reducers

Analysis of the difference between footprints of uncertainty for interval type-2 fuzzy PI controllers