Optimal workplacement for robotic friction stir welding task

Robotic manipulators are widely used in industry for welding processes. Inadequate joint stiffness in the manipulators often limits their use for high quality welding operations because of the deformation errors produced during the process. As a matter of fact, welding quality deteriorates with decreasing joint stiffness. This paper presents an approach to determine an optimal workspace of operation by minimizing the lateral deflection errors in position and orientation of the end effector during Friction Stir Welding. This has been done by estimating the errors in position and orientation of the end effector, also the point of contact with work piece which directly affects welding quality, when it experiences a wrench during welding operation. The technique was applied to an elastodynamic model of a 6 DOF manipulator with different path constraints for welding process to achieve optimal task placement. In a nutshell, optimal starting position or an optimal direction of motion for best welding quality can be precisely computed or even both together can be calculated but with numerical complexity.ANR COROUSS

Compliance error compensation in robotic-based milling

The paper deals with the problem of compliance errors compensation in robotic-based milling. Contrary to previous works that assume that the forces/torques generated by the manufacturing process are constant, the interaction between the milling tool and the workpiece is modeled in details. It takes into account the tool geometry, the number of teeth, the feed rate, the spindle rotation speed and the properties of the material to be processed. Due to high level of the disturbing forces/torques, the developed compensation technique is based on the non-linear stiffness model that allows us to modify the target trajectory taking into account nonlinearities and to avoid the chattering effect. Illustrative example is presented that deals with robotic-based milling of aluminum alloy

Kinematic analysis and optimization of robotic milling

Robotic milling is proposed to be one of the alternatives to respond the demand for flexible and cost-effective manufacturing systems. Serial arm robots offering 6 degrees of freedom (DOF) motion capability which are utilized for robotic 5-axis milling purposes, exhibits several issues such as low accuracy, low structural rigidity and kinematic singularities etc. In 5-axis milling, the tool axis selection and workpiece positioning are still a challenge, where only geometrical issues are considered at the computer-aided-manufacturing (CAM) packages. The inverse kinematic solution of the robot i.e. positions and motion of the axes, strictly depends on the workpiece location with respect to the robot base. Therefore, workpiece placement is crucial for improved robotic milling applications. In this thesis, an approach is proposed to select the tool axis for robotic milling along an already generated 5-axis milling tool path, where the robot kinematics are considered to eliminate or decrease excessive axis rotations. The proposed approach is demonstrated through simulations and benefits are discussed. Also, the effect of workpiece positioning in robotic milling is investigated considering the robot kinematics. The investigation criterion is selected as the movement of the robot axes. It is aimed to minimize the total movement of either all axes or selected the axis responsible of the most accuracy errors. Kinematic simulations are performed on a representative milling tool path and results are discusse

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone

Optimal Robot Placement for Tasks Execution

AbstractAutomotive assembly cells are cluttered environments, including robots, workpieces, and fixtures. Due to high volumes and several product variants assembled in the same cell, robot placement is crucial to increase flexibility and throughput. In this paper, we propose a novel method to optimize the base position of an industrial robot with the objective to reach all predefined tasks and minimize cycle time: robot inverse kinematics and collision avoidance are integrated together with a derivative-free optimization algorithm. This approach is successfully used to find feasible solutions on industrial test cases, showing up to 20% cycle time improvement

Optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms

Abstract Process planning of multi-robot cells is usually a manual and time consuming activity, based on trials-and-errors. A co-manipulation problem is analysed, where one robot handles the work-piece and one robot performs a task on it and a method to find the optimal pose of the work-piece is proposed. The method, based on a combination of Whale Optimization Algorithm and Ant Colony Optimization algorithm, minimize a performance index while taking into account technological and kinematics constraints. The index evaluates process accuracy considering transmission elasticity, backslashes and distance from joint limits. Numerical simulations demonstrate the method robustness and convergence

A stiffness-based quality measure for compliant grasps and fixtures

This paper presents a systematic approach to quantifying the effectiveness of compliant grasps and fixtures of an object. The approach is physically motivated and applies to the grasping of two- and three-dimensional objects by any number of fingers. The approach is based on a characterization of the frame-invariant features of a grasp or fixture stiffness matrix. In particular, we define a set of frame-invariant characteristic stiffness parameters, and provide physical and geometric interpretation for these parameters. Using a physically meaningful scheme to make the rotational and translational stiffness parameters comparable, we define a frame-invariant quality measure, which we call the stiffness quality measure. An example of a frictional grasp illustrates the effectiveness of the quality measure. We then consider the optimal grasping of frictionless polygonal objects by three and four fingers. Such frictionless grasps are useful in high-load fixturing applications, and their relative simplicity allows an efficient computation of the globally optimal finger arrangement. We compute the optimal finger arrangement in several examples, and use these examples to discuss properties that characterize the stiffness quality measure

Compliance error compensation in robotic-based milling

The paper deals with the problem of compliance errors compensation in robotic-based milling. Contrary to previous works that assume that the forces/torques generated by the manufacturing process are constant, the interaction between the milling tool and the workpiece is modeled in details. It takes into account the tool geometry, the number of teeth, the feed rate, the spindle rotation speed and the properties of the material to be processed. Due to high level of the disturbing forces/torques, the developed compensation technique is based on the non-linear stiffness model that allows us to modify the target trajectory taking into account nonlinearities and to avoid the chattering effect. Illustrative example is presented that deals with robotic-based milling of aluminum alloy

Optimized joint motion planning for redundant industrial robots

The paper presents a model and solution method for optimized robot joint motion planning of redundant industrial robots that execute a set of tasks in a complex work environment, in face of various technological and geometric constraints. The approach aims at directly exploiting redundancy to optimize a given performance measure, e.g., cycle time. Alternative configurations along the path are captured in a graph model, whereas bi-directional transition between task and configuration spaces facilitates generating relevant, collision-free configurations only. Re-parametrization of the trajectory warrants compliance with the robot's kinematic constraints. Successful application of the method is demonstrated in remote laser welding. (C) 2016 CIRP