Trajectory Planning Of Robot Research Articles

Robot Trajectory Planning and Simulation Based on Matlab Robotics Toolbox

Robot Trajectory Planning and Simulation Based on Matlab Robotics Toolbox

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Quasi Time-Fuel Optimal Control Strategy for Dynamic Target Tracking

Time and fuel balance is an important topic in the dynamic tracking problem of unmanned systems. In this paper, we propose a quasi time-fuel optimal control strategy (QTFOC) to solve the dynamic tracking problem of a double integrator system, which is capable of multi-target switching tracking tasks, such as the multi-target strike of weapons and the rapid multi-target grabbing of robots on industrial assembly lines. Compared with the previous optimal control algorithms, the proposed controller retains the time-fuel optimal characteristic when switching among multiple dynamic targets, and overcomes the high-frequency oscillation problem by incorporating local linear control region and two nonlinear buffer areas. That is, when switching among multiple dynamic targets, the proposed control strategy enables the corresponding system to achieve desired transient performance and satisfactory steady-state performance simultaneously. In addition, the asymmetry of friction load is further explored, which affects the dynamic performance of the actual system. Extensive experiments based on the visual tracking turntable demonstrate the superiority and feasibility of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of multi-target switching tracking, such as the multi-target strike of weapons with vision sensors and the rapid multi-target grabbing of robots on industrial assembly lines. In recent years, various algorithms have been developed for trajectory planning and tracking. However, it is still challenging for unmanned servo system to capture dynamic targets quickly due to the constrains of motion performance, energy, and computational power. In order to reduce the computational burden and obtain ideal response under various physical constraints, a quasi time-fuel optimal control strategy (QTFOC) with analytical solutions is proposed in this paper. First, the static target is extended to the dynamic target by further investigating the traditional time-fuel optimal control theory for double integrator system. Second, to deal with the oscillation problem caused by system disturbances, we incorporate buffer areas and local linear control region into the control algorithm, which makes the system more robust. Third, the system performance is further improved by analyzing the frictional load asymmetry. This algorithm requires small computational resources and can be implemented directly on microcontrollers such as STM32. The experimental results demonstrate the superiority of our proposed method in handling the multi-target switching tracking problem, which can also adjust the response speed weight, making the unmanned system perform better under different operating conditions. Furthermore, the proposed QTFOC can be extended to other unmanned systems, such as trajectory planning and motion control for unmanned vehicles and bionic robots.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Multi-objective trajectory planning for segment assembly robots using a B-spline interpolation- and infeasible-updating non-dominated sorting-based method

The rapid and smooth functioning of segment assembly robots, which is always conflicting, is critical to improving efficiency and ensuring safety during tunneling construction, particularly for the series-actuated robots employed in non-circular shield machines. However, the trade-off between the aforementioned goals has not been explored for trajectory planning in joint space. Less is known about how to acquire superior trade-off Pareto solutions for this constrained multi-objective optimization problem. To fill this gap, this paper proposes a B-spline interpolation- and infeasible-updating non-dominated sorting-based method for multi-objective trajectory planning of shield machine segment assembly robots. In particular, a multi-objective optimization model is detailed in terms of time, acceleration, and jerk of joint trajectories while accounting for operational efficiency and motion smoothness. The given objective functions can be determined using B-spline interpolation and time information based on the known via-points of hydraulic joints. Meanwhile, the constraints are transformed into a limited number of control point-derived forms. Following that, the infeasible-updating non-dominated sorting-based evolutionary algorithm (INSEA) is introduced to solve this problem and find Pareto-optimal solutions. The main improvement is that the multi-objective function information of infeasible solutions is utilized so as to update the non-dominated sorting process, which adaptively applies population division and individual replacement. The findings indicate that the proposed method is capable of executing multi-objective trajectory planning during different stages of the assembly procedure, and the computed metrics are all greater than serval advanced algorithms. Furthermore, on the basis of the proposed INSEA, multi-degree B-spline interpolation provides lower acceleration peaks and smoother global trajectories than common cubic spline curves throughout the process. Therefore, this method can provide researchers with a wide range of alternatives to achieve the optimum trade-off for multi-objective trajectory planning.

A novel multi-seam extraction method for structured workpieces with medium-thick plates based on DLP vision

Currently, most seam extraction methods focus on some simple application scenarios. For structured workpieces with medium-thick plates, which have unknown and various structures, the current methods are difficult to deal with. Based on DLP (Digital Light Processing) vision, this paper proposes a novel multi-seam extraction method for structured workpieces. Firstly, the DLP vision system is constructed to achieve single-view data acquisition for workpieces. Next, we propose an improved registration method to fuse point clouds collected from multiple perspectives. Finally, the positions and poses of seams are solved by the designed extraction algorithm. The experimental results demonstrate the proposed multi-seam extraction method can accurately extract seams in a single perspective, and achieve coarse extraction for seams distributed in multiple perspectives, which can be further provided for robot trajectory planning and laser sensing to achieve fully automatic welding.

Developing a Static Kinematic Model for Continuum Robots Using Dual Quaternions for Efficient Attitude and Trajectory Planning

Kinematic modeling is essential for planning and controlling continuum robot motion. The traditional Denavit Hartenberg (DH) model involves complex matrix multiplication operations, resulting in computationally intensive inverse solutions and trajectory planning. Solving position and orientation changes in continuum robots using the double quaternion rule can reduce computational complexity. However, existing dual quaternion methods are direct equational transformations of DH rules and do not give a complete modeling process. They usually require more interpretability when applying continuum robot kinematic modeling. This paper uses the dual quaternion method to establish a kinematic model of a continuum robot. It uses a two-section continuum robot model to compare the advantages of dual quaternion and traditional modeling methods. In addition, this paper proposes a five-polynomial interpolation algorithm based on the dual quaternion method for trajectory planning of continuum robots. This method accurately models spatial bending and torsional motions of singularity-free continuum robots.

Trajectory Planning of Rail Inspection Robot Based on an Improved Penalty Function Simulated Annealing Particle Swarm Algorithm

Trajectory Planning of Rail Inspection Robot Based on an Improved Penalty Function Simulated Annealing Particle Swarm Algorithm

Dynamic modeling, trajectory planning and prototyping of an ice-skating robot

Dynamic modeling, trajectory planning and prototyping of an ice-skating robot

Design, modelling, implementation, and trajectory planning of a 3-DOF cable driven parallel robot

Cable tensions in cable robots make trajectory planning more complicated than in rigid-link robots. Since cables can only pull but not push, the cable tensions must be kept positive for a cable-driven system to maintain control. In this paper direct methods of trajectory planning including direct collocation and direct shooting are proposed to solve the two-point boundary value problem trajectory planning. Minimizing a robot's actuator force under cable constraints including positive forces and preventing severe changes in tensions are considered in this problem. The cable-driven robot consists of a 3-cable and a pneumatic cylinder, which betters robot tensionability. The direct methods are solved by the sequential quadratic programming algorithm and then compared with the GPOPS-II software package. The results designate that the direct methods of trajectory planning propose substantial benefits in satisfying essential continuity and smoothness of resulted profiles for a cable-driven robot with a multi-type actuation system. Experimental results confirm the numerical data, additional supporting the findings.

Digital twin model-based smart assembly strategy design and precision evaluation for PCB kit-box build

Research concerning microelectronics assembly has attracted increasing attention from the manufacturing industry. In this context, achieving precise placement of the printed circuit board (PCB) inside the enclosure is the most critical aspect of the kit-box build assembly task. However, automating the PCB kit-box build assembly (PKBA) process remains a challenging work. This study presents a Digital Twin (DT) model that enables accurate perception of object pose for intelligent PKBA assembly, facilitating real-time monitoring and evaluation of its service status. In our system, a symmetry-drive method is proposed to optimize the initial pose in 6-DoF matching during DT assembly. Based on the developed technology, a three-stage learning method is established to achieve grasping point localization and robotic assembly trajectory planning. To ensure accurate and robust robot assembly, we developed a PKBA quality prediction model based on the DT system, which predicts the uncertainty of actual PCB assembly positioning by small displacement torsor (SDT) theory and Monte Carlo methods. Particularly, the assembly quality of the actual product is effectively monitored when the state of the virtual simulation model corresponds to the physical assembly object. Finally, a prototype system and a case study involving dexterous assembly tasks are conducted to verify the effectiveness and feasibility of the proposed method. The results indicate that the proposed PKBA strategy achieves an 82% assembly success rate. By employing well-designed strategies, our method ensures that the majority of errors are below 0.8 mm and 0.6 degrees.

Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.

Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles

Despite recent progress in trajectory planning for multiple robots and a single tethered robot, trajectory planning for multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this article, a complete approach is presented to address this problem. First, a multirobot tether-aware representation of homotopy is proposed to efficiently evaluate the feasibility and safety of a potential path in terms of 1) the cable length required to reach a target following the path, and 2) the risk of entanglements with the cables of other robots. Then the proposed representation is applied in a decentralized and online planning framework, which includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free, and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against the existing single and multiple tethered robot planning approaches. Simulations with up to eight UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using three tethered UAVs verify the practicality of the presented approach. The software implementation is publicly available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Modified Dynamic Movement Primitives: Robot Trajectory Planning and Force Control Under Curved Surface Constraints.

Dynamic movement primitives (DMPs) have been widely applied in robot motion planning and control. However, in some special cases, original discrete DMP fails to generalize proper trajectories. Moreover, it is difficult to produce trajectories on the curved surface. To solve the above problems, a modified DMP method is proposed for robot control by adding the scaling factor and force coupling term. First, the adjusted cosine similarity is defined to assess the similarity of the generalized trajectory with respect to the demonstrated trajectory. By optimizing the similarity, the trajectories can be generated in all situations. Next, by adding the force coupling term derived from adaptive admittance control to the transformation system of the original DMP, the controller achieves the force control ability. Then, the modified DMP-based robot control system is developed. The stability and convergence of the system are proved. Finally, the high precisions of the proposed method are verified by simulations and experiments. The method is significant for trajectory learning and generalization on the curved surface.

Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

Trajectory planning is a crucial step in controlling robot motion. The efficiency and accuracy of trajectory planning directly impact the real-time control and accuracy of robot motion. The robot’s trajectory is mapped to the joint space, and a mathematical model of trajectory planning is established to meet physical constraints during motion and avoid joint coupling problems. To enhance convergence speed and avoid local optima, an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning. The trajectory planning in robot joint space is then tested based on the improved quantum particle swarm optimization algorithm. The results demonstrate that this method can replace the traditional trajectory planning algorithms and offers higher accuracy and efficiency.

Trajectory planning of a dual-arm space robot for target capturing with minimizing base disturbance

During the on-orbit capture task of the free-floating space robot, its operating space degenerates. The dynamic coupling and dynamic singularity also affect the task operation. In this paper, a general collision-free trajectory planning method to minimize base disturbance is proposed for a free-floating dual-arm space robot. This method can realize smooth joint trajectory planning while avoiding singularity and realizing self-collision avoidance. The singularity can be effectively avoided by transforming the trajectory planning problem into a nonlinear optimization problem. The optimization function is composed of base disturbance penalty function, end effector error function and collision repulsion potential field function, which can be used to solve the optimal joint trajectory parameters. Futhermore, self-collision avoidance is achieved by combining the bacterial foraging optimization algorithm(BFOA) with the geometric vector collision-avoidance method based on artificial potential field to simplify the calculation complexity. Finally, simulations verify the effectiveness of the proposed planning framework.

An Asymmetric Collision-Free Optimal Trajectory Planning Method for Three DOF Industrial Robotic Arms

To improve the speed and dynamic adaptability of robotic arm trajectory planning, a collision-free optimal trajectory planning method combining non-uniform adaptive time meshing and bounding box collision detection was proposed. First, the dynamics and objective function of the asymmetric industrial robotic arm with three degrees of freedom (DOF) was formulated in the form of the dynamic optimization problem. Second, the control vector parameterization (CVP) was improved to enhance the computational performance of the problem. The discrete grid was adaptively adjusted according the trend of control variables. Then, a quick and effective collision detection strategy was used to avoid obstacles and to speed up calculation efficiency. The non-collision constraint is built by transforming the collision detection into the distance between two points, and then is combined into the dynamic optimization problem. The solution of the new optimization problem with the improved CVP leads to the higher calculation performance and the avoidance of obstacles. Lastly, the Siemens Manutec R3 robotic arm is taken as an example to verify the effectiveness of the planning method. The approach not only reduces computation time but also maintains accurate calculations, so that optimal trajectory can be selected from symmetric paths near the obstacles. When weights were set as λ1 = λ2 = 0.5, the solution efficiency was improved by 33%, and the minimum distance between the robotic arm and obstacle could be 0.08 m, which ensured that there was no collision.

Motion trajectory planning of a mobile robot flying in a fuzzy environment

Abstract.This study involved implementing a fuzzy controller approach to create a ground-based control model for an autonomous robot in a stable environment. In addition, a classical control method was developed and tested within the results. With the fuzzy controller approach, it was studied that the robot moves easily in the test environment and goes to desired targets. An autonomous mobile robot aims to minimize the energy consumption of the robot with a fuzzy controller algorithm. The energy consumption for processing time and data error between the coordinates where the robot should go and the coordinates it goes after the operations are completed are calculated separately. By utilizing a fuzzy controller algorithm, the mobile robot is capable of operating in a flexible manner. It was observed that the fuzzy approach performs 10 times faster and more accurately compared to the traditional method. In terms of being open to development and modular, the flying mobile robot fulfilled the functions envisaged in this work. In the future, the direction of a flying robot can be determined using a digital electronic compass to reach longer distances with external sensors.

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

PurposeThe paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.Design/methodology/approachThe proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.FindingsThe proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.Originality/valueThe novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.