Path Coordination Planning and Control in Robotic Material Handling and Processing

This chapter presents a unified approach to coordination planning and control for robotic position and orientation trajectories in Cartesian space and its applications in robotic material handling and processing. The unified treatment of the end-effector positions and orientations is based on the robot pose ruled surface concept and used in trajectory interpolations. The focus of this chapter is on the determination and control of the instantaneous change laws of position and orientation, i.e., the generation and control of trajectories with good kinematics and dynamics performances along such trajectories. The coordination planning and control is implemented through controlling the motion laws of two end points of the orientation vector and calculating the coordinates of instantaneous corresponding points. The simulation and experiment in robotic surface profiling/finishing processes are presented to verify the feasibility of the proposed approach and demonstrate the capabilities of planning and control models. Keywords: Robot pose ruled surface, Unified approach, Trajectory planning and control, Off-line programming, Robotics polishin

Optimizing task placement in robotic cells

The primary objective of this dissertation is to develop novel and practical techniques for optimal task placement in robotic cells. To this end, it is shown how task placement affect the efficiency of the cell, whether the task is automated fiber placement to create composite materials, gluing or inspection. Here, efficiency of the cell is defined by either cycle time of the production or distance to singularity, having collision avoidance as a constraint. Task placement, even for one robotic arm, is an under-constrained problem in nature. This issue drastically grows in case of redundant robotic cells. Actuator redundancy in robotic cells is added by either a positioner or another manipulator. This work is focused on taking advantage of redundancy in robotic cells and optimizing it for better performance. One of the main challenges here is to identify the number of independent placement parameters. Therefore, we ignore ineffective variables and only focus on minimum number of parameters possible. Hence, faster optimization process and more precise results are obtained. Another challenge is in motion planning of redundant cells. Because there can be infinite solutions for such cells, there is room for optimization. In this work, we propose methods to fix the optimal placement of the task and, furthermore, assign the optimal motion planning to all manipulators in the cell, simultaneously. A novel method is proposed to identify the number of independent parameters and applied to a gluing path for a coordinated redundant robotic workcell. The workcell consists of a generic six-DOF serial manipulator and a one-DOF redundancy provider (RP). Two cases of RPs are investigated, namely a rotary table and a linear guide. An innovative method using swept volume is proposed for determining the number of independent parameters for both cases under study. The outcome of this study is an intuitive method to identify the number of independent parameters in redundant cells. The results are compared between using all initial parameters, as contrary to only the independent ones. It is proven that the proposed method improves the optimization efficiency by 32%. Moreover, the performance of the rotary table is compared to the linear guide, for a specific gluing application. Optimization methods in this work are based on Particle Swarm Optimization (PSO). A workcell consisting of a six degrees of freedom (DOF) serial manipulator, a six-DOF parallel manipulator and a rotary table mounted on the parallel manipulator is studies for automated fiber placement task. The solution to motion planning is obtained considering the singularities of the serial manipulator and the workspace boundaries of all manipulators. The algorithm to obtain the optimum path placement is explained through a simple example and the results for a helix path with nearly 2,700 points around the workpiece is represented. The results for motion planning are represented where distance to singularity is maximized, collision avoidance and workspace boundaries are respected. The result is obtained after 10 iterations with 20 particles. This outcome of this study is a reliable and easy to apply motion planning algorithm to be used in redundant cells. Another challenge in this work is combinatorial task placement that arises in robotic inspection cells. The goal is to improve the efficiency of a turbine blade inspection cell through optimizing the placement of the camera and optimizing the sequence of the images. The workcell contains a six-DOF serial manipulator that is holding the blade and shows it to the camera from different angles, whereas the camera takes inspection images. The problem at hand consists of a six-DOF continuous optimization for camera placement and discrete combinatorial optimization of sequence of images (end-effector poses). A novel combined approach is introduced, called Blind Dynamic Particle Swarm Optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. Our objective is to minimize the cycle time, while avoiding any collisions in the workcell during the inspection operation. Even though PSO is vastly used in engineering problems, novelty of the proposed combinatorial optimization method is in its ability to be used efficiently in the traveling salesman problems where the distances between cities are unknown (blind) and the distances are subject to change (dynamic). This highly unpredictable domain is the case of the inspection cell where the cycle time between images will change for different camera placements. The cycle time is calculated based on weighted joint travel time of the robot. All the eight configurations of the robot are taken into the consideration, therefore, robot’s configuration is optimized in the final result as well. The outcome of this study is an innovative hybrid algorithm to simultaneously solve combinatorial and continues problems. Results show fast convergence and reliable motions. The test of benchmarks selected from TSPLIB shows that the results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness than those obtained by other methods. The best placement of camera and best image sequence (for 8 images) is obtained after 11 iterations using 30 particles. In general, the main results of this thesis are three algorithms: an algorithm to obtain minimum number of placement parameters in redundant robotic workcells; an algorithm for motion planning of highly redundant cells; and an algorithm to optimize camera placement and simultaneously obtain the optimal image sequence in an inspection cell