Optimal trajectory planning and control of industrial robot based on ADAM algorithm of nonlinear data set

With the continuous extension of the application scenarios of industrial robots, industrial robots have made great contributions to increase productivity and automation in the industrial field. And trajectory planning of industrial robots is the key link for its successful completion of tasks. In order to deeply analyze the mechanism of trajectory planning of industrial robots, this paper firstly introduces the principle of trajectory optimization. And then it reviews the relevant theories and methods of trajectory planning. Finally, it summarizes the development trend of trajectory planning of industrial robots.

PurposeThis paper aims to present an optimal trajectory planning for industrial MOTOMAN MA1440A gas metal arc welding system. A new and efficient evolutionary algorithm, enhanced multi-objective teaching learning-based optimization (EMOTLBO) method, i.e. TLBO with non-dominated sorting approach has been proposed to obtain the optimal joint trajectory for the defined weld seam path.Design/methodology/approachThe joint trajectory of the welding robot need to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the weld path and for achieving higher positional accuracy. This can be achieved by limiting the kinematic and dynamic variations of the robot joints like joint jerks, squared acceleration and torque induced in the joints while travel of the robot along the weld path. Also, the robot travel should be done within minimum possible time for maintaining productivity. This leads to a multi-objective optimization problem which needs to be solved for maintaining proper orientation of the robot end effector. EMOTLBO has been proposed to obtain the Pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the Pareto front with best trade-off between objectives.FindingsThe proposed method has been implanted in MATLAB R2017a for simulation results. The joint positions have been used to program the robot for performing welding operation along the weld seam. From the simulation and experimental results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of MOTOMAN MA 1440 A arc welding robot system as a very smooth and uniform weld bead has been obtained with maximum weld quality.Originality/valueIn this paper, a novel approach for optimal trajectory planning welding arc robot has been performed. Though trajectory planning of industrial robots has been done before, it has not been done yet for welding robot. The objectives are formulated taking in consideration of requirement of welding process like minimization of joint jerks and torques induced during welding operation due to travel of robot with the effect of arc spatter, minimization of squared acceleration for maintaining constant joint velocity and finally minimization of total travel time for maintaining productivity.

This paper proposes new method based on evolutionary algorithm for optimal trajectory planning of robot manipulator (PUMA 560). The intelligent method is elitist non dominated sorting based genetic algorithm (NSGA-II). This method is superior as it considers multiple criterion to be optimised simultaneously. The multicriterion cost function has to minimize with defined constraints. The problem has 5 objective functions, 32 constraints and 252 variables. Pareto optimal is set of compromise solutions instead of single optimal solution. Pareto optimal fronts are evaluated by the solution spread measure and ratio of non dominated individuals. The B-spline function is used to define robot trajectories.

Purpose The purpose of this paper is to improve the positional accuracy, smoothness on motion and productivity of industrial robot through the proposed optimal joint trajectory planning method. Also a new improved algorithm, i.e. non-dominated sorting genetic algorithm-II (NSGA-II) with achievement scalarizing function (ASF) has been proposed to obtain better optimal results compared to previously used optimization methods. Design/methodology/approach The end effector positional errors can be reduced by limiting the uncertainties of dynamic parameter variations like torque rate of joints. The jerk induced in robot joints due to acceleration variations are need to be minimized which otherwise induces vibrations in the manipulator that causes deviation in the encoders. But these lead to a vast increase in total travel time which affects the cost function of trajectory planning. Therefore, these three objectives need to be minimized individually so that an optimal trajectory path can be achieved with minimum positional error. Findings The simulation results have been obtained by running the proposed hybrid NSGA-II with ASF in MATLAB R2017a software. The optimal time intervals have been used to calculate jerk, acceleration and torque values for consecutive points on the trajectory path. From the simulation and experimental results, it can be concluded that the optimization technique could be used effectively for the trajectory planning of six-axis industrial manipulator in the joint space on the basis of minimum time-jerk-torque rate criteria. Originality/value In this paper, a new approach based on hybrid multi-objective optimization technique by combining NSGA-II with ASF has been applied to find the minimal time-jerk- torque rate joint trajectory of a six-axis industrial robot for obtaining higher positional accuracy. The results obtained from the execution of algorithm have been validated through experimentation using Kawasaki RS06L industrial robot for a particular defined path.

This paper addresses the challenge of simultaneously satisfying collision-free trajectory, path quality optimization, and execution time optimization in trajectory planning for industrial robots in confined spaces. A multi-objective integrated trajectory optimization algorithm is proposed based on the TD3 reinforcement learning framework. Firstly, the motion generation mechanism is improved using a Butterworth filter and a dynamic noise attenuation strategy to enhance trajectory smoothness. Secondly, a genetic algorithm is employed to automatically optimize the hyperparameters of the TD3 algorithm, and a prioritized experience replay mechanism is introduced to improve the utilization efficiency of critical experiences, thereby enhancing the algorithm's convergence speed and stability. Finally, a composite reward function based on time-distance information is designed to effectively guide the industrial robot in optimizing trajectory execution time under collision-free conditions. This paper uses the Fairino Robot5 robotic arm model as the training object and conducts simulation experiments in a PyBullet environment, as well as real physical experiments. Compared to the RRT method, manual teaching method, traditional TD3 method, and SAC method, the actual trajectory execution time was reduced by 57.03% , 22.94% , 26.05%, and 20.5%, respectively.

This paper presents a trajectory generation and data collection scheme for the 3-D visualization of geometric parts by an industrial manipulator. The implementation of the proposed scheme consists of a simulation environment constituting by a set of state-of-the-art software libraries and tools. Experimental results, obtained with a five DOF augmented SCARA industrial robot manipulator, show the effectiveness of the proposed scheme.

Time-Torque Coordinated Optimization for Trajectory Planning of Industrial Robots

This article addresses the issue of discontinuities in joint acceleration and torque during the time-optimal trajectory planning of industrial robots. It proposes the use of logarithmic aesthetic curves to smooth out abrupt curvature changes in the velocity profile, ensuring the smoothness of the velocity profile and reducing the sudden changes in joint acceleration and joint torque. The effectiveness of the algorithm is ultimately validated through simulations.

This paper discusses the optimal trajectory planning problem of multibody systems. The aim of this study is to develop a general purpose optimal trajectory planning algorithm to be applied to arbitrary multibody systems. Multibody systems may be divided into two groups, i.e. open loop systems and closed loop systems [8]. In [11] an optimal trajectory planning algorithm for open loop systems was presented. In this paper, optimal trajectory planning algorithms for closed loop systems are proposed by extending the algorithm for open loop systems. Two types of methods are presented based on the dynamic analysis by computational algorithms for closed loop systems. The first method uses generalized coordinate partitioning and embedding techniques. The second method is based on an augmented formulation with Lagrange multipliers. The first method is easily applicable to non-redundant actuation systems, while the second method considers redundant actuation. The validity of these methods for optimal trajectory planning is confirmed by computational results and their features are compared.

Geometric characteristics of geodesics in the Riemannian surface are used to make robotic optimal trajectory planning in this paper. Distance length and kinetic energy are regarded as Riemannian metrics respectively, and the Riemannian surfaces are determined by the corresponding metrics, and they represent the robotics kinematics and dynamics respectively. The geodesies on the Riemannian surface are calculated and are regarded as the optimal trajectory. Geodesic is the necessary condition of the shortest length between two points on the Riemannian surface and the covariant derivative of the geodesic's tangent vector is zero. When to implement optimal trajectory planning with arc length as the Riemannian metric, geodesic makes the shortest length between two points. The end-effector's velocity is invariant along the geodesic and the acceleration is zero. So the motion is very smooth. When system's kinetic energy as the Riemannian metric, the geodesic between two points on the kinetic surface makes the kinetic energy remain invariant. Computer calculation and simulation verify that the method based on geodesic is good at trajectory planning especially when the trajectory is linear or certain index should be minimized.

In order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

This paper presents an algorithm for trajectory planing using potential fields and swarms with vortex behavior. This algorithm is called Vortex Swarm Path Planning Algorithm (VSPP). There are several methods for trajectory planning in robotics, the most popular is the one based on artificial potential fields. However this method has the disadvantage of local minima which can make the robot fail when reaching the destination point. The proposed algorithm here presented employs a model of active Brownian particles with vortex behavior that allows the trajectory planning evading local minima.

For the complex system modeling of operating underwater robots, various modules are built in Simulink to simulate the interaction of various forces and torques inside the underwater vehicle. According to the characteristics of the ROV itself, the ROV dynamics model is reasonably designed. In view of the problems of the position tracking delay, underwater environment interference, and unstable operation of the ROV, the fuzzy fractional PID control algorithm is adopted in the controller in this paper, and the control effect of the fuzzy PID and PID algorithm is compared and analyzed.

Taking the Tai-Chi Push-hands Mobile Robot as the research object, the corresponding theoretical research has been carried out, and a lot of theoretical analysis, model simulation and experimental debugging have been done. The simulation system and the solid model platform are built. The trajectory planning, kinematics, information acquisition and control of humanoid robot are studied and analyzed. The main contents are as follows: (1) Research on PID based on ant colony algorithm. (2) Tai-Chi push-hands arm system based on STM32 platform is built. (3) Construction of a humanoid robot system based on Kinect depth camera. In order to achieve better results in human-computer interaction, Kinect and Nao robots are used as the research objects, and Kinect depth camera is used to collect human posture information, which is transmitted to Nao robots, and the effect of human-computer tracking is preliminarily completed.

Trajectory planning and control of bipedal walking robots require precise joint torque computation to ensure stability and efficiency. Given the nonlinear dynamics and complex interactions of bipedal systems, achieving stable walking remains a major challenge. Deep reinforcement learning (DRL) offers a promising solution by directly mapping observed states to optimal actions that maximize cumulative rewards. In this work, we integrate deep learning-based trajectory planning with a DRL-driven control system to generate optimal joint torque sequences. Our approach aims to achieve stable walking with maximum forward speed, minimal power consumption, and enhanced stability to prevent falls. After training, the bipedal robot demonstrates stable and resilient locomotion, maintaining balance throughout the gait cycle. Additionally, it exhibits robust performance under uncertainties, handling mass variations of up to 20% and length variations of up to 5%. The robot effectively rejects disturbances at different angular velocities across various gait phases, enhancing adaptability. This approach improves the robustness and efficiency of bipedal robots, making them more suitable for real-world applications requiring reliable and adaptive locomotion.