Energy efficient trajectory planning by a robot arm using invasive weed optimization technique

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the predefined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.

The aim of the research work is to propose an integrated optimization technique, established with the integration of the invasive weed optimization (IWO) and Powell’s pattern search (PPS) method. The IWO algorithm has been undertaken as a global search technique, which is inspired from the specific ecological behavior of weeds and has the ability to adapt to the changing environment. The local search PPS method is based upon a conjugate-based search and having excellent exploitation search capability, which helps to improve the solution obtained from IWO technique. The proposed technique is applied to solve optimal power flow (OPF) problem with the flexible AC transmission system devices. The OPF problem is a nonlinear, non-convex optimization problem and consists of continuous and discrete decision variables. The three objective functions comprise total fuel cost, pollutant emission and system transmission loss which are minimized sequentially. The proposed technique is tested on four standard IEEE test systems, and results are compared with the reported results in the literature and found promising. The results illustrate that the proposed technique performs better as compared to IWO technique in terms of the quality of solution and convergence characteristics. Further, t test is performed to validate the statistical performance of the proposed integrated optimization technique.

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.

The aim of this paper is to apply Invasive Weed Optimization (IWO), Jaya Algorithm (JA) and a proposed Jaya-based Invasive Weed Optimization (JIWO) technique to the tuning of proportional-integral-derivative (PID) controllers for load frequency control (LFC) and to determine which method is more appropriate. The Matlab/Simulink environment is used for the implementations and simulations. Taking into account performance measures, such as peak values, settling times and steady-state errors, as well as the optimality of the solutions and the convergence rates, the most suitable approach is selected. The results obtained show that all of the three optimization techniques are able to deliver improved responses over the uncompensated system. However, JIWO provides the best results, both in terms of convergence and optimality.

An ecologically inspired optimization algorithm, called invasive weed optimization (IWO), is presented for the design of non-uniform, planar, and circular antenna arrays that can achieve minimum side lobe levels for a specific first null beamwidth while avoiding the mutual coupling effects simultaneously. IWO recently emerged as a derivative-free real parameter optimizer that mimics the ecological behavior of colonizing weeds. For the present application, classical IWO has been modified by introducing a more explorative routine of changing the standard deviation of the seed population (equivalent to mutation step-size in evolutionary algorithms) of the algorithm. Simulation results over three significant instances of the circular array design problem have been presented to illustrate the effectiveness of the modified IWO algorithm. The design results obtained with modified IWO have been shown to comfortably beat those obtained with other state-of-the-art metaheuristics like genetic algorithm (GA), particle swarm optimization (PSO), original IWO and differential evolution (DE) in a statistically meaningful way.

In this paper, we propose the minimum energy control input based on Hamiltonian strategy for the motor-table system. The energy equation of the motor-table system contains of the input energy, dissipation energy, potential energy and output energy, and includes electrical and mechanical energies. The methods of the minimum control effort (MCE), minimum input energy control (MIEC), minimum dissipation energy control (MDEC) and trapezoidal trajectory energy control are compared for the motor-table system. From the numerical results, we can find that the MIEC and MDEC approaches can obtain the same minimum input energy. Traditionally, the MCE approach was regarded as the minimum energy control in the previous study. However, we have demonstrated that the MCE approach is not the minimum energy control in this paper.

In this contribution a novel invasive weed optimization (IWO) based multi user detector (MUD) aided time-hopping ultra-wide band (TH-UWB) system has been investigated in the indoor channel model. In this approach, the IWO based MUD employs the output of the Rake receiver as its initial value to search for the best solution which results on a formulated optimization mechanism. By taking advantage of the heuristic values and the collective intelligence of IWO technique, the proposed detector offers almost the same bit error rate (BER) performance as the full-search-based optimum multi-user detector does, while greatly reducing the potentially computational complexity. The good behavior of the proposed approach is demonstrated by means of comparisons in terms of BER performance and implementation complexity with the classical Rake receiver and different multi-user receivers previously proposed in the literature of this subject. Simulation results have been provided to examine the evolutionary behavior and the detection performance of the proposed IWO based MUD in both the additive white Gaussian noise (AWGN) and the multi-path fading channel.

In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO) algorithm which has population diversity with the heuristic global search of differential evolution (DE) algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO) algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations.

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although this field is still in its early stages, its impressive potential has led to the development of various LLM-based EAs. To effectively evaluate the performance and practical applicability of these LLM-based EAs, benchmarks with real-world relevance are essential. In this paper, we focus on LLM-based recommender systems (RSs) and introduce a benchmark problem set, named RSBench, specifically designed to assess the performance of LLM-based EAs in recommendation prompt optimization. RSBench emphasizes session-based recommendations, aiming to discover a set of Pareto optimal prompts that guide the recommendation process, providing accurate, diverse, and fair recommendations. We develop three LLM-based EAs based on established EA frameworks and experimentally evaluate their performance using RSBench. Our study offers valuable insights into the application of EAs in LLM-based RSs. Additionally, we explore key components that may influence the overall performance of the RS, providing meaningful guidance for future research on the development of LLM-based EAs in RSs.

Diffusion mechanisms of four intrinsic point defects in rutile TiO2, titanium interstitial (TiI), titanium vacancy (Vti), oxygen interstitial (OI) and oxygen vacancy (VO) are studied in the framework of density functional theory with quantum ESPRESSO suite. Diffusion processes are simulated by defect movement between two stable atomic configurations through using the climbing image nudged elastic band (CI-NEB) method.The initial and final atomic structure in the minimum energy path (MEP) are constructed with 3×3×4 perfect supercell matrix of 216 atoms. Considering that oxygen atoms build up TiO6 octahedron and half of the octahedral centers are occupied by Ti atoms in rutile, interstitial defect is constructed by adding one Ti or O atom to the empty oxygen octahedral center, and vacancy defect is constructed by removing one atom from crystal lattice grid. Structural relaxation is performed before performing the NEB calculation with gamma k point sampling in irreducible Brillouin zone with an energy cutoff of 650 eV. As rutile TiO2 has tetragonal symmetry (P42/mnm), the diffusion channel along the[100] direction is equivalent to the[010] direction. Then, the diffusion paths along the direction parallel to c axis ([001] direction) and perpendicular to the c axis ([100] or[110] direction) are chosen to find the minimum diffusion energy path of TiI and OI. As for VTi and VO, diffusion paths are established from the vacancy site to nearest lattice site of the same kind.Calculation results exhibit significant anisotropy of energy barrier and diffusion mechanism. Of all defect species, TiI diffusion along the[001] direction through interstitial mechanism has the lowest activation barrier of 0.5057 eV. In addition, diffusions along the[100] and[110] direction through kick-out mechanism show higher energy barriers of 1.0024 eV and 2.7758 eV, respectively. Compared with TiI, OI shows small barrier discrepancy between different diffusion directions, which is 0.859 eV along[001] and 0.902 eV along[100] direction. For vacancy defects, diffusion can occur only through the vacancy mechanism. The activation barrier energy of symmetrically inequivalent diffusion path of VO is 0.735 eV along the[110] direction, 1.747 eV along the[001] direction, and 1.119 eV from the TiO6 apex site to the equator site. On the other hand, VTi has two inequivalent paths with much larger diffusion energy barriers:2.375 eV along the[111] direction and 3.232 eV along the[001] direction. In summary, the TiI interstitial diffusion along the[001] direction (parallel to the c axis) has the lowest activation barrier in rutile TiO2, which is in excellent agreement with former experimental and theoretical data.

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.

This paper deals with the application of a heuristic-based evolutionary algorithms (EAs) on a specific industrial problem - the scheduling of rapid transit systems. The system under consideration is a medium-sized mass rapid transit (MRT) system and the dual objectives of minimizing operating costs and passenger dissatisfaction are considered. Making use of concepts such as Pareto-optimality and multiobjective evolutionary algorithms, the authors applied a multiobjective evolutionary algorithm (MOEA) to solve the problem. Comparison studies were done with current method, obtaining satisfactory results.

The robot's arm used to be limited to manipulation by the industrial manipulator, and most studies on the humanoid robot focus on robots' walking methodologies. Since the multi-DOF (degree of freedom) system, like the humanoid robot's arm, has a high- dimension configuration space that cannot appear geometrically and that has many kinematics and dynamics constraints, the path planning of the system requires much computation and, as such, is very complicated. In this paper, the path planning algorithm was employed for the humanoid robot's (Maru-1) arms. The RRT (rapidly exploring random tree) algorithm, which has been proven to be an efficient approach to multi-arm motion plans, was selected for use in this study. The employed algorithm, with inverse-kinematics analysis, was evaluated in this study using 3D graphic simulation.

In this paper, we use a compact 6 degree-of- freedom (DOF) robot arm as the actuator of the venipuncture system. Aiming at the robot arm’s requirements for speed and smoothness and considering that the robot arm may collide with obstacles in the course of movement,we simplify the model of the robot arm and obstacles according to the geometry of the robot arm and obstacles in the environment. Then we proposed an improved A-star algorithm and a three-dimensional (3D) collision detection algorithm, which can plan a collision-free optimal path for the blood taking needle fixed in the end of the robot arm, so that the blood taking needle can penetrate into the target point of the venous vessel continuously,smoothly and accurately driven by the robot arm. The simulation and actual path planning experiments indicate that the proposed path planning method is effective and feasible.

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.