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This 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.

The 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.

The 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.

In 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.

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.

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.

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.

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.

The purpose of this paper is to propose an efficient path and trajectory planning method to solve online robotic multipoint assembly.

A path planning algorithm called policy memorized adaptive dynamic programming (PM-ADP) combines with a trajectory planning algorithm called adaptive elite genetic algorithm (AEGA) for online time-optimal path and trajectory planning.

Experimental results and comparative study show that the PM-ADP is more efficient and accurate than traditional algorithms in a smaller assembly task. Under the shortest assembly path, AEGA is used to plan the time-optimal trajectories of the robot and be more efficient than GA.

The proposed method builds a new online and efficient path planning arithmetic to cope with the uncertain and dynamic nature of the multipoint assembly path in the Cartesian space. Moreover, the optimized trajectories of the joints can make the movement of the robot continuously and efficiently.

The proposed method is a combination of time-optimal path planning with trajectory planning. The traveling salesman problem model of assembly path is established to transfer the assembly process into a Markov decision process (MDP). A new dynamic programming (DP) algorithm, termed PM-ADP, which combines the memorized policy and adaptivity, is developed to optimize the shortest assembly path. GA is improved, termed AEGA, which is used for online time-optimal trajectory planning in joints space.

The 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.

The 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.

The 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.

The 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.

This paper aims to present a technique for optimal trajectory planning of industrial robots that applies a new harmony search (HS) algorithm.

The new HS optimization algorithm adds one more operation to the original HS algorithm. The objective function to be minimized is the trajectory execution time subject to kinematical and mechanical constraints. The trajectory is built by quintic B‐spline curves and cubic B‐spline curves.

Simulation experiments have been undertaken using a 6‐DOF robot QH165. The results show that the proposed technique is valid and that the trajectory obtained using quintic B‐spline curves is smoother than the trajectory using cubic B‐spline curves.

The proposed new HS algorithm is more efficient than the sequential quadratic programming method (SQP) and the original HS method. The proposed technique is applicable to any industrial robot and yields smooth and time‐optimal trajectories.

The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.

This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.

The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.

The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm.

The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory.

Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method.

The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.

Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel).

The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot.

The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller.

Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular integrated construction (MiC).

Through a rigorous systematic mixed-review methodology that integrates bibliometric, scientometric and qualitative analysis, this study explored the crane layout research trend; the scientometric analysis of journal sources and keywords occurrence network; the research contributions and links between influential countries; the classification of research articles based on the type of problems and solution approaches; the qualitative analysis of existing findings and research gaps; and the future research direction for CLP in MiC.

This study found five categories under the CLP domain, namely, crane selection, crane location, integrated crane selection and location, integrated crane location and allocation of supply points and hybrid problems. The major research approaches used to solve CLP is optimization (43%), visualization (23%), decision support systems (16%), simulation (11%) and qualitative techniques (7%). The possible future research directions include artificial intelligence-based models, multi-crane locations, CLP for MiC re-use, dynamic models representing real-life scenarios and building information modeling-based virtual reality models.

Through a mixed-review methodology, this study provides a comprehensive analysis of problem settings and solution methods of CLP while mitigating the subjectivity of traditional review methods. Also, it presents a repertoire on CLP and illuminates future directions for seasoned researchers in the context of MiC.

The purpose of this paper is to analyze the impact of the torque, power, jerk and energy consumed constraints on the generation of minimum time collision‐free trajectories for industrial robots in a complex environment.

An algorithm is presented in which the trajectory is generated under real working constraints (specifically torque, power, jerk and energy consumed). It also takes into account the presence of obstacles (to avoid collisions) and the dynamics of the robotic system. The method solves an optimization problem to find the minimum time trajectory to perform the tasks the robot should do.

Important conclusions have been reached when solving the trajectory planning problem related to the value of the torque, power, jerk and energy consumed and the relationship between them, therefore enabling the user to choose the most efficient way of working depending on which parameter he is most interested in optimizing. From the examples solved the authors have found the relationship between the maximum and minimum values of the parameters studied.

This new approach tries to model the real behaviour of the actuators in order to be able to upgrade the trajectory quality, so a lot of work has to be done in this field.

The algorithm solves the trajectory planning problem for any industrial robot and the real characteristics of the actuators are taken into account, which is essential to improve the performance of it.

This new tool enables the performance of the robot to be improved by combining adequately the values of the mentioned parameters (torque, power, jerk and consumed energy).