Multi-objective trajectory optimization method for industrial robots based on improved TD3 algorithm.

In order to better realize the optimal trajectory planning and trajectory control in industrial robots, a method based on ADAM algorithm is proposed. Taking PUMA 560 industrial robot as the research object, using nonlinear data sets and mathematical ADAM algorithm function planning, an optimal calculation method for time trajectory planning of industrial robot is explored. Finally, the programming, optimization, and simulation of the program code are implemented using MATLAB, and a standardized optimal trajectory planning is established. The experimental results show that the running time difference of the trajectory corresponding to the three joint points is small. In order to synchronize the position of each joint point in time, it is necessary to choose the optimal joint point position according to the time trajectory, so as to ensure the synchronization between each key node. Therefore, the joint node position is adjusted so that the total time and the final simulation results are basically synchronized in time, and both are 10.35 s. It proves that the improved ADAM algorithm realizes the trajectory optimization of industrial robots in terms of time planning, which can make the various joints of industrial robots basically synchronized in the time trajectory.

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.

PurposeThis 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueIn 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.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

This paper presents a trajectory generation and data collection scheme for the 3-D visualization of geometric parts by an industrial manipulator. The implementation of the proposed scheme consists of a simulation environment constituting by a set of state-of-the-art software libraries and tools. Experimental results, obtained with a five DOF augmented SCARA industrial robot manipulator, show the effectiveness of the proposed scheme.

This paper proposes new method based on evolutionary algorithm for optimal trajectory planning of robot manipulator (PUMA 560). The intelligent method is elitist non dominated sorting based genetic algorithm (NSGA-II). This method is superior as it considers multiple criterion to be optimised simultaneously. The multicriterion cost function has to minimize with defined constraints. The problem has 5 objective functions, 32 constraints and 252 variables. Pareto optimal is set of compromise solutions instead of single optimal solution. Pareto optimal fronts are evaluated by the solution spread measure and ratio of non dominated individuals. The B-spline function is used to define robot trajectories.

Time-Torque Coordinated Optimization for Trajectory Planning of Industrial Robots

This article addresses the issue of discontinuities in joint acceleration and torque during the time-optimal trajectory planning of industrial robots. It proposes the use of logarithmic aesthetic curves to smooth out abrupt curvature changes in the velocity profile, ensuring the smoothness of the velocity profile and reducing the sudden changes in joint acceleration and joint torque. The effectiveness of the algorithm is ultimately validated through simulations.

With the rapid development of unmanned aerial vehicle (UAV) technology, its applications in military and civilian fields are becoming increasingly widespread. However, realizing autonomous navigation of UAVs in complex environments still faces numerous challenges, especially the three-dimensional path planning problem. This paper proposes an improved parrot optimization algorithm (IPO) by introducing SPM chaotic mapping, adaptive switching factor, and hybrid Cauchy and Gaussian mutation strategies to enhance the algorithm's global search capability and convergence speed. The improved IPO is combined with the MATLAB simulation platform to construct a complete three-dimensional path-planning solution framework. Extensive simulation experiments demonstrate that, compared with the standard parrot optimization and other optimization algorithms, this algorithm significantly improves optimization accuracy, convergence speed, and path smoothness, showing good potential for engineering applications. The experimental results indicate that under the complex three-dimensional environment modeling of the Beijing International Studies University (BISU) campus, the improved IPO algorithm can quickly and accurately find the optimal collision-free path with minimal flight cost, which demonstrates excellent prospects for engineering applications.

In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to tackle the nondeterministic polynomial hard problem stemming from inventory fibre resources and fibre selection principles in optical cable production. The algorithm aims to maximize inventory score and minimize fibre segmentation rate. It employs a permutation encoding approach to link the genetic algorithm with fibre allocation solutions and applies a self-attention mechanism to determine subset solution weight within each solution. To boost the recombination of favourable gene segments from different chromosomes, a rule optimization strategy is integrated into the crossover operation based on the weights. This operations enhance the algorithm's global search capability and convergence speed. A feasibility repair strategy is then used to inspect and rectify chromosomes, preventing the generation of illegal solutions. The legitimate mutation operation, founded on weight optimization rules, effectively reduces the algorithm's running time by avoiding illegal solutions. By leveraging actual production data from an optical cable manufacturer for simulation, the experimental results confirm the effectiveness of the improved genetic algorithm in addressing the fibre allocation problem. Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.

Abstract. With the advancement of urbanization, urban-rural public transport issues have become one of the most critical issues in urban development. The paper makes a detailed study on the optimization of urban-rural bus routes in Erqi District of Zhengzhou, China. The factors of bus stop selection are analyzed, and the three categories, including traffic road condition factors, economic benefit factors and waiting number factors, are mainly considered. The analytic hierarchy process is used to determine 35 specific objectives of urban-rural bus stop optimization, 20 of which are selected for simulation experiment with large weight. Then the ant colony optimization (ACO) algorithm in path optimization is analyzed, which has the following two advantages. First, the global pheromone update is combined with the local pheromone update to enhance the algorithm's optimization ability and convergence speed. Second, through the method of spatial contraction transformation, the ant constructs a solution to reduce the number of construction steps and speed up the operation. Based on the actual analysis of urban-rural public transportation in the Erqi District of Zhengzhou, a simulation experiment is executed to show that the ACO algorithm is able to find out the optimal path, which is 15.1% shorter than the ant colony system (ACS) algorithm. The ACO algorithm improved path planning has good time effectiveness and path practicability.

This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning.

International audience

Path planning plays a vital role in robot navigation and manipulation, and multiple types of algorithms have been introduced to address this problem. Rapidly-exploring Random Tree (RRT) based algorithms have many advantages over other path planning algorithms. For example, RRT is suitable to solve the path planning problem in high dimensional space and can easily handle robot differential constraints. Informed RRT* is a method that uses the prolate hyper-spheroid to speed up the optimization process, but its efficiency will decrease to the same level as RRT* when the hyper-spheroid covers most of the state space. To overcome this drawback, we further propose a Sliding-Window Informed RRT* (SWIRRT*), which combines the sliding-window thought into the Informed RRT*, taking the advantage of the initial path and make the path optimization much faster. Simulations in 2D space have been carried out to demonstrate that our proposed method can improve the RRT-like algorithm's convergence speed.