Time-Impact Optimal Trajectory Planning for Wafer-Handling Robotic Arms Based on the Improved Snake Optimization Algorithm.

Time-optimal trajectory planning for shipboard welding robotic arms is a challenging problem due to strong kinematic constraints and the nonlinear coupling between trajectory parameters and execution time. Although various intelligent optimization algorithms have been combined with robotic arm trajectory planning in existing studies, most approaches primarily focus on algorithmic performance improvement and lack a clear formulation of time optimization within polynomial trajectory planning. To address this gap, this study proposes an Improved Whale Optimization Algorithm (IWOA) based on the traditional quintic polynomial trajectory planning method. In the proposed method, the trajectory execution time is explicitly formulated as the optimization objective under kinematic constraints, and the IWOA is designed to stably and efficiently search the time parameter space of the quintic polynomial trajectory. Specifically, chaotic sequence initialization is employed to enhance population distribution, an adaptive weight mechanism is introduced to balance global exploration and local exploitation, and a hybrid co-optimization strategy combining differential evolution and genetic operators is integrated to improve robustness and convergence stability. Simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. The results demonstrated that, while satisfying robotic arms kinematic constraints, the proposed method achieves an 18.3% reduction in operating time compared with the unoptimized trajectory. These results indicate that the proposed approach provides a systematic and effective solution for time-efficient trajectory planning of shipboard welding robotic arms.

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (IDBO) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the IDBO algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance.

Geosteering based on resistivity data and evolutionary optimization algorithm

This paper is oriented to the transmission line inspection environment, aiming to solve the problem of how to control the robotic arm to quickly and accurately grasp the target object, and work on the planning of the motion path and the position-based visual servo control method. For the spatial trajectory planning of the robotic arm, linear trajectory planning and circular arc trajectory planning are carried out respectively. Between the reaction speed problem of the traditional RRT motion algorithm in collision-free motion planning, a two-way RRT-Connect path planning method is proposed. Then the visual servo control algorithm of the UAV robotic arm is proposed. The position-based visual servo control method is adopted, while the EK-SVSF position estimation algorithm and the OWA-based stepwise data fusion algorithm are combined for optimal position estimation of the target during transmission line inspection. It is verified in simulation experiments that the improved RRT algorithm can obtain the shortest path planning in a shorter time with better performance. Finally, the grasping experiment is carried out in a real robotic arm transmission line inspection scene. The robotic arm path planning algorithm and visual servo control proposed in this paper are combined to carry out grasping experiments in a static cluttered environment. The experimental results show that the algorithm proposed in this paper has the best grasping success rate, servo success rate, and grasping similarity. In addition, the average errors in x-axis, y-axis and z-axis of the proposed method are 0.19cm, 1.11cm and 0.18cm, respectively, which show a good grasping accuracy, indicating that the control method proposed in this paper can be applied to the real transmission line inspection work scenarios.

Robots are used to provide help in unsafe, repetitive and dreary situations. This paper concentrates on a robotic arm which can be utilized for crossing of the dead wire structure to the jumper cable of an electrical transmission line. In order to achieve a good positional accuracy of the end-effector, the robotic arm is modeled and analyzed. Denavit-Hartenberg (DH) representation is used for the kinematic modeling and Lagrangian-Euler (LE) method for the dynamic modeling. Since the kinematic and dynamic modeling of robotic arm is highly nonlinear in nature, the optimum trajectory planning is a challenging issue in almost all obstacle avoidance problems. In trajectory planning, the desired trajectory needs to be followed for end-effector positioning. This paper presents kinematic modeling, dynamic modeling, workspace analysis and trajectory planning of a 5 Degree of freedom (DOF) robotic arm. The robotic arm is designed using SolidWorks 2014 software. The complete analysis of the proposed manipulator is simulated using MATLAB Software.

In this paper, we propose an approach for real-time collision-free trajectory planning of multiple robot manipulators in a common workspace. In recent years, robot arms are often introduced to factories in place of human beings, and it has become important how efficiently multiple robot arms can be operated in a small space. The problem of trajectory planning for multiple robot arms is often solved by graph search algorithms, however, it is difficult for the conventional approach to provide flexible trajectory planning to cope with unexpected situations such as robot arm failure. Therefore, we propose a combined method for Q-learning and RRT for the trajectory planning problem. The effectiveness of the proposed method is further verified using numerical experiments. The planned trajectories is able to guarantee a certain degree of optimality when the motion trajectory is generated by combining reinforcement learning than by using only the graph search algorithm. The results indicate that the time required to generate the motion trajectory is reduced by the proposed method.

A large number of factors influence the choice of production system for a particular oil and gas field. This paper outlines the key drivers that govern the selection process for one subset of systems-surface facilities for deepwater production- and verifies them using a comprehensive database of deepwater platforms. Regional variations-natural, industrial, political and experiential-are seen to be particularly important in the selection process. For fields benefiting from platform drilling and/or workover, the water depth and well count are key drivers. The matrix approach to system comparison presented in the paper also highlights future technology developments across a range of disciplines required for more efficient hydrocarbon production in ultra-deepwater. Both new platform types, and enhancements of existing facilities to extend their applicability, are described. Overall, subsea well systems are anticipated to find increasing application as water depths increase. Introduction Offshore oil and gas fields may be developed using a variety of production systems. The configuration of the optimum system for a particular application depends on a myriad of factors [1]. First, the selection criteria used to determine the "best" solution must be defined-these are likely to be lifecycle financial value as well as other more subjective measures. The field characteristics are important, including site, regional, global and company parameters, as are the elements of the preferred production plan to deplete the reservoir. The hardware components required to deliver the production plan have inherent advantages and limitations that must be correlated with each other and to the field and production parameters; system selection involves optimising these synergies. Potential risks associated with the system must also be recognised. However, of these many factors, only a few become the key drivers that govern the final selection decision, although these key drivers vary according to the field and production characteristics. The system selection process is greatly facilitated when the key drivers are understood. This paper describes a sequence of key drivers for facility selection in deepwater. It is presented in tabular form and justified using a database of past, current and sanctioned deepwater platforms. Anticipated innovations are also discussed briefly to appreciate how these might influence both the range of facility choices available, and the selection process. Deepwater Facilities "Deepwater" is defined in the study to be greater than 300m (nearly 1,000ft), giving a reasonable sample size of 89 facilities. This cut-off enables all compliant towers to be included, along with some important North Sea platforms. Conventional shallow water fixed platform technology is ignored even though both jackets and concrete gravity structures have been installed in water depths greater than 300m. Subsea tiebacks to existing facilities are also excluded. This leaves six proven deepwater platform types-FPSOs, semisubmersibles, spars, compliant towers, TLPs and mini-TLPs [2-4]. Here, the mini-TLP is differentiated from conventional TLPs by supporting subsea trees, with the first three mono-column TLPs being of this type. Table 1 summarises key statistics of the various facility types; a detailed comparison is presented in [1].

The research was tenaciously conducted to model and simulate a robot arm using MATLAB Simulink software. The research activities included modification of a robot arm that mimics the controlling performance of a real human arm in MATLAB and the controlling effects of each joint as per the angle of rotation of the robot arm. The controlling moment (forces) of the robot arm was mathematically modeled. Euler-Lagrange equation of motion was applied to determine vector of control moments (forces). During the simulation process, the first to achieve was to model equations of motion in MATLAB symbolically and then (further) to generate function blocks directly in Simulink followed by use of Sims cape toolbox to model the robot arm similar to 3D modeling. The robot arm end effecter followed certain reference trajectory as computed torque chosen in feed forward plus PD (Proportional Derivative) controller in feedback division. Effective control of the robot arm was ensured by connecting the robot arm to a personal computer (PC) using a microcontroller. The complete control process was accomplished through two divisions of CUBLOC microcontroller program (to write code of interference between PC and the robot arm) and MATLAB Program (containing serial communication code and the graphical user interface). The stages employed in the activities of the complete system functions consist of kinematics, trajectory planning and a controller. The output was tested and it showed that the simulated robot arm performed the dynamic activities, particularly the control, of a real human arm in the software environment.

In high-dimensional scenarios, trajectory planning is a challenging and computationally complex optimization task that requires finding the optimal trajectory within a complex domain. Metaheuristic (MH) algorithms provide a practical approach to solving this problem. The Crayfish Optimization Algorithm (COA) is an MH algorithm inspired by the biological behavior of crayfish. However, COA has limitations, including insufficient global search capability and a tendency to converge to local optima. To address these challenges, an Enhanced Crayfish Optimization Algorithm (ECOA) is proposed for robotic arm trajectory planning. The proposed ECOA incorporates multiple novel strategies, including using a tent chaotic map for population initialization to enhance diversity and replacing the traditional step size adjustment with a nonlinear perturbation factor to improve global search capability. Furthermore, an orthogonal refracted opposition-based learning strategy enhances solution quality and search efficiency by leveraging the dominant dimensional information. Additionally, performance comparisons with eight advanced algorithms on the CEC2017 test set (30-dimensional, 50-dimensional, 100-dimensional) are conducted, and the ECOA's effectiveness is validated through Wilcoxon rank-sum and Friedman mean rank tests. In practical robotic arm trajectory planning experiments, ECOA demonstrated superior performance, reducing costs by 15% compared to the best competing algorithm and 10% over the original COA, with significantly lower variability. This demonstrates improved solution quality, robustness, and convergence stability. The study successfully introduces novel population initialization and search strategies for improvement, as well as practical verification in solving the robotic arm path problem. The results confirm the potential of ECOA to address optimization challenges in various engineering applications.

The problem of trajectory planning is one of the important tasks of autonomous mobile robots (AMR). For that, in this paper, a trajectory planning algorithm is developed to generate a smooth trajectory for AMR in a static environment. The generated trajectory links a starting point with an end point while avoiding static obstacles. The trajectory curve is parameterized with the B-spline interpolation due to its smoothness properties and to reduce the number of points needed to define. Therefore, to find the optimal B-spline parameters, the trajectory planning problem has turned into an optimization problem. Improved Grey Wolf Optimizer (I-GWO) was selected as optimization algorithm because of its excellent optimization performance and global search capability. Different I-GWO settings were tested and the best selected setting was compared with other optimization algorithms. The results show that the selected optimization algorithm is more efficient and accurate.

For the loading robot developed in this project, due to the unreasonable time allocation between target points and points, which leads to the waste of overall time, the excessive energy consumption caused by heavy load, and the vibration and impact caused by rapid stop, an appropriate trajectory planning method is proposed to make it move smoothly, and its trajectory planning is optimized based on harmony search algorithm. The trajectory planning optimization takes time and energy consumption as the performance index of harmony search algorithm optimization. The weighted parameters are allocated according to the actual situation and the mathematical model is established according to the actual constraints of the robot. Then, the algorithm is improved according to the actual model. Finally, the graph obtained by algorithm optimization shows that the working time is shorter and the energy consumption is smaller after optimization, which verifies the effectiveness and significance of robot trajectory planning optimization.

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

The paper proposes a new M2ACD(Multi-Actor-Critic Deep Deterministic Policy Gradient) algorithm to apply trajectory planning of the robotic manipulator in complex environments. First, the paper presents a general inverse kinematics algorithm that transforms the inverse kinematics problem into a general Newton-MP iterative method. The M2ACD algorithm based on multiple actors and critics is structured. The dual-actor network reduces the overestimation of action values, minimizes the correlation between the actor and value networks, and mitigates instability during the actor’s selection process caused by excessively high Q-values. The dual-critic network reduces the estimation bias of Q-values, ensuring more reliable action selection and enhancing the stability of Q-value estimation. Secondly, The robotic manipulator’s TSR (two-stage reward) strategy is designed and divided into the approach and close. Rewards in the approach phase focuses on safely and efficiently approaching the target, and rewards in the close phase involves final adjustments before contact is made with the target. Thirdly, to solve the position hopping jitter problem in traditional reinforcement learning trajectory planning, the NURBS(Non-Uniform Rational B-Splines) curve is used to smooth the hopping trajectory generated by M2ACD. Finally, the correctness of the M2ACD and the kinematics algorithm is verified by experiments. The M2ACD algorithm demonstrated superior curve smoothing, convergence stability and convergence speed compared to the TD3, DARC and DDPG algorithms. The M2ACD algorithm can be effectively applied to collaborative robots’ trajectory planning, establishing a foundation for subsequent research.

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algorithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assignment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate association cost; hence, much of the procedure time is saved. In the 2-stage association algorithm, a large number of false location points are eliminated from candidate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.

To address issues of local optimization, slow convergence, and poor accuracy, this work introduces a time-optimal trajectory planning method using the Adaptive Gold Search algorithm (AGS). The method enhances algorithm diversity and escapes local optima through elite reverse strategy population initialization. It employs an adaptive nonlinear decreasing factor for a balance between linearity and nonlinearity, improving accuracy and convergence speed. The golden sinusoidal variation technique enhances global search capabilities and robustness in robot arm trajectory planning. Experimental results show that the AGS algorithm produces smooth joint angular displacement, acceleration, velocity, and jerk curves. It outperforms the GA, PSO, GOOSE, and GWO algorithms in time optimization by 13.31%, 13.61%, 5.34%, and 4.73%, respectively, effectively reducing trajectory planning time.