Trajectory Planning Of Robot Research Articles

The application of Deep Learning in the field of Robotics

Abstract. As a new field with rapid development in the past two decades, deep learning has received more and more attention from researchers. Neural network adopts widely interconnected structure and effective learning mechanisms to simulate the process of information processing in the human brain, which is an important method in the development of artificial intelligence. Compared with shallow models, deep learning can achieve stronger unsupervised autonomous learning capabilities by increasing the number of layers of the network. In the past, the robots needed to be controlled by the humans; they controlled the robot to finish different tasks. In recent years, the researchers have tried to apply deep learning in the field of robots. Deep neural networks can simulate the complex cognitive laws of the human brain, so many researchers apply deep learning to robots to make robots have the thinking ability of humans. This article will review the cases of combining robot and in-depth learning in recent decades, Summarizing the achievements and problems faced in the three fields of robot vision, trajectory planning and motion control.

Application of artificial intelligence in the new generation of underwater humanoid welding robots: a review

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Study on robot trajectory planning and coating thickness prediction for plasma spraying on complex surface

Plasma spraying techniques are commonly employed for the deposition of thermal barrier coatings (TBCs) due to their efficiency and cost-effectiveness. However, ensuring uniform coating thickness and quality on complex free-form surfaces poses significant challenges. This paper investigates the influence of spraying trajectory and related parameters (spraying distance, angle, velocity) on coating thickness distribution, addressing the need for simplified analysis among numerous variables affecting coating quality. Different from the predominantly existing research focusing on flat or rotationally symmetric substrates, this study delves into the planning of spray trajectories for free-form surfaces, which is crucial for industries dealing with complex components, such as turbine blades. Innovative optimization approaches are employed to refine spray trajectories and improve coating consistency. Through theoretical modeling, simulation and experimental validation, the impact of spray parameters on coating thickness was demonstrated. Both the mean and disperssion coefficient errors of the coating thickness, obtained by the theoretical prediction model and the spray experiments, are lower than 10 %. The normal spray trajectory makes the coatings more evenly distributed, and the coating uniformity is at least 50 % higher than that of the codirectional spraying. This research contributes to the optimization of plasma spraying processes, particularly on irregular surfaces, thereby facilitating the development of high-performance TBCs for industrial applications.

Flexible control and trajectory planning of medical two-arm surgical robot.

This paper introduces the flexible control and trajectory planning medical two-arm surgical robots, and employs effective collision detection methods to ensure the safety and precision during tasks. Firstly, the DH method is employed to establish relative rotation matrices between coordinate systems, determining the relative relationships of each joint link. A neural network based on a multilayer perceptron is proposed to solve FKP problem in real time. Secondly, a universal interpolator based on Non-Uniform Rational B-Splines (NURBS) is developed, capable of handling any geometric shape to ensure smooth and flexible motion trajectories. Finally, we developed a generalized momentum observer to detect external collisions, eliminating the need for external sensors and thereby reducing mechanical complexity and cost. The experiments verify the effectiveness of the kinematics solution and trajectory planning, demonstrating that the improved momentum torque observer can significantly reduce system overshoot, enabling the two-arm surgical robot to perform precise and safe surgical tasks under algorithmic guidance.

A review of the trajectory planning of Industrial robots

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.

Holographic direct sound printing

Direct sound printing (DSP), an alternative additive manufacturing process driven by sonochemical polymerization, has traditionally been confined to a single acoustic focal region, resulting in a voxel-by-voxel printing approach. To overcome this limitation, we introduce holographic direct sound printing (HDSP), where acoustic holograms, storing cross-sectional images of the desired parts, pattern acoustic waves to induce regional cavitation bubbles and on-demand regional polymerization. HDSP outperforms DSP in terms of printing speed by one order of magnitude and yields layerless printed structures. In our HDSP implementation, the hologram remains stationary while the printing platform moves along a three-dimensional path using a robotic arm. We present sono-chemiluminescence and high-speed imaging experiments to thoroughly investigate HDSP and demonstrate its versatility in applications such as remote ex-vivo in-body printing and complex robot trajectory planning. We showcase multi-object and multi-material printing and provide a comprehensive process characterization, including the effects of hologram design and manufacturing on the HDSP process, polymerization progression tracking, porosity tuning, and robotic trajectory computation. Our HDSP method establishes the integration of acoustic holography in DSP and related applications.

Multi-objective trajectory planning and implementation of a metamorphic palletizing robot

Multi-objective trajectory planning and implementation of a metamorphic palletizing robot

Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks.

Path optimization of underwater vehicles in multi-obstacle environment based on energy constraint strategy

Purpose To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy. Design/methodology/approach Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment. Findings The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles. Originality/value The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

Trajectory Planning Research Based on Rapidly-exploring Random Trees (RRT) Algorithm and Modified Forms

With the rapid advancement of computer technology, robots have become indispensable in various fields. Trajectory planning plays a crucial role in optimizing robot performance by enhancing work efficiency and reducing operational costs. While the widely used Rapidly-exploring Random Trees (RRT) algorithm is effective in path planning, it often generates non-optimal paths with poor smoothness. By incorporating elements from RRT-Connect and reverse optimization, the algorithm can efficiently navigate through intricate and dynamic surroundings. Furthermore, the utilization of cubic polynomial interpolation in optimizing path curves adds a layer of sophistication to the trajectory planning process. This interpolation technique allows for the creation of smoother and more natural path trajectories, reducing jerky movements and enhancing overall motion control precision. The paths produced by the enhanced RRT algorithm demonstrate increased continuity and visual attractiveness, crucial for tasks necessitating accurate and elegant robotic motions. The improved algorithm showcases reduced path length and time consumption, as well as enhanced path smoothness. Particularly, relative to the traditional RRT approach, the time consumption has dropped by 69.15% and the median duration of the paths has fallen by 21.11%. These improvements signify a substantial advancement in trajectory planning for robots, offering more efficient and smoother paths for robotic operations across various applications.

Research on Gait Trajectory Planning of Wall-Climbing Robot Based on Improved PSO Algorithm

Research on Gait Trajectory Planning of Wall-Climbing Robot Based on Improved PSO Algorithm

Trajectory planning and simulation of upper limb rehabilitation robot based on NSGA2

In order to increase the stability and comfort of hemiplegic rehabilitation training, this paper integrates B-spline curves with a non-dominated sorting genetic algorithm (NSGA2) for trajectory planning of a 5-DOF upper limb exoskeleton rehabilitation robot, the structural characteristics of the robot are analyzed. Subsequently, the forward kinematics equation is derived by the D-H method, and its correctness is verified by Adams simulation. Subsequently, the robot workspace is calculated, and the outcomes show that it satisfies the patient’s demand for activity. Finally, the trajectory curve is constructed using the 5th-order B-splines. Under kinematic constraints, the NAGA2 is used to discover the ideal time interval sequence for time, impact and energy, and then complete the trajectory planning. The simulation findings demonstrate the suggested trajectory planning method produces continuous, smooth, and high-performance motion trajectories, providing good rehabilitation training for patients.

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

This paper addresses the issue of trajectory planning for a free-floating dual-arm space robot, considering the constraints of collision avoidance while simultaneously minimizing the base position and attitude disturbance during on-orbit tasks. One arm is designated as the mission arm, tracking a prescribed path to complete specific goals, while the other serves as the balance arm, compensating for any base attitude disturbance. A collision-free path planning approach is presented for the mission arm, which utilizes a hybrid map, improved artificial potential field (IAPF) and adaptive coupling guidance method. The hybrid map in the mission arm joint space contains the coupling relationship between the deviation of the base position and the motion of the dual arms, along with obstacles transformed from Cartesian space. Then, to address the problem of the goals nonreachable with obstacles nearby (GNRON) of the artificial potential field (APF), a novel repulsive potential field is incorporated into the IAPF. To minimize the base position disturbance, a coupling guidance method is presented that enables the mission arm to move in areas with lower coupling factors on the hybrid map. Finally, simulations are conducted to demonstrate the effectiveness and superiority of the proposed hybrid map and coordinated trajectory planning approach.

Research on trajectory learning and modification method based on improved dynamic movement primitives

Traditional robot trajectory planning and programming methods often struggle to adapt to changing working requirements, leading to repeated programming in manufacturing processes. To address these challenges, a trajectory learning and modification method based on improved Dynamic Movement Primitives (DMPs), called FDC-DMP, is proposed. The method introduces an improved force-controlled dynamic coupling term (FDCT) that uses virtual force as coupling force. This enhancement enables precise and flexible shape modifications within the target trajectory range. The paper also dissects the core dynamic systems of DMP to achieve the reproduction and generalization of both robot position and pose trajectories. The practical feasibility of the proposed method in manufacturing is demonstrated through two case studies on trajectory planning for bus body polishing.

Model-Based Trajectory Planning of a Hybrid Robot for Powerline Inspection

Model-Based Trajectory Planning of a Hybrid Robot for Powerline Inspection

Path and trajectory planning of a RPRPR planar parallel robot for prevention of high-order singularities

Parallel robots have many remarkable advantages over their conventional serial counterparts. Highly accurate positioning capability and high payload-to-weight ratio are among the main ones. The main factor underlying all these advantages is their closed-loop construction. However, this constructional feature also causes a special singularity problem, which constitutes their biggest disadvantage. Characteristic singularities classified as Type II exist inside their workspace, around which the magnitude of the inverse dynamic solution grows unboundedly. This naturally yields the saturation of the actuators and eventually in uncontrollability of the robot. Consequently, the whole workspace becomes impossible to be used. In order to pass through a Type II singularity, the consistency of the motion equations of the robot must be maintained at that singularity. However, any Type II singular configuration can transform into a high-order singularity, which, in order to be passed through, requires additional conditions other than the consistency. Therefore, full utilization of the whole workspace with a minimum number of conditions requires to avoid high-order singularities. The present article contributes to the literature by developing path and trajectory planning principles for preventing a two-degree-of-freedom planar parallel robot with RPRPR structure from experiencing high-order singularities.

Design of control system for six-degree-of-freedom robotic arm

To study the robotic arm trajectory planning and motion, the six-degree-of-freedom robotic arm control system design is proposed. In this paper, we choose a multi-joint type of robotic arm. We analyze the design of the robotic arm at home and abroad and determine the specific servomotor model and the specific robotic arm mechanism. We analyze the torque relationship of each joint and the size of the servomotor. Using SolidWorks and Robot Studios, we carry out the three-dimensional design modeling and simulation of the robotic arm, design the whole set of robotic arms, and use the Arduino Uno development board for precise control of the servo motor. The control program was written using C language, and various control tests were carried out on the robotic arm to make sure that the system could satisfy the debugging and research of trajectory planning type.

Variable Time-Step Physics Engine with Continuous Compliance Contact Model for Optimal Robotic Grinding Trajectory Planning.

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks.

Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating

Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating

Trajectory planning of tire laser engraving orthogonal robot based on an improved multi-objective grasshopper optimization algorithm

The traditional tire laser engraving robots have the low and stable running capability and discontinuous motion trajectory during operation, leading to long running time, high energy consumption, and uneven operation. In order to solve these problems, this paper proposes an orthogonal robot trajectory planning method based on an improved multi-objective grasshopper optimization algorithm (IMOGOA) with the orthogonal robot as the research object. Firstly, this paper used the quintic non-uniform rational b-splines (NURBS) curves to construct the trajectory. Secondly, the optimization objectives of the orthogonal robot’s running time, energy consumption, and smoothness were used to obtain the Pareto optimal solution by using the IMOGOA to establish the objective function. Then, the optimization method was simulated and analyzed, and the optimal solution was selected to compare with the rest of the non-dominated solution sets. The simulation results showed that the proposed method reduced the running time by 33.68%, improved the energy consumption and smoothness performance by 34.48% and 56.58%, and achieved multi-objective comprehensive optimization of the orthogonal robot. Thus, the effectiveness and feasibility of the proposed method were verified. Finally, a tire laser engraving orthogonal robot experimental platform was built for physical verification experiments. The experimental results showed that the experimental trajectory deviated from the simulated trajectory by only 5.63%, further proving the proposed method’s practicality.