Manipulator system man-machine interface evaluation program

Application and requirements for remote manipulator systems for future space missions were investigated. A manipulator evaluation program was established to study the effects of various systems parameters on operator performance of tasks necessary for remotely manned missions. The program and laboratory facilities are described. Evaluation criteria and philosophy are discussed

Earth orbital teleoperator system man-machine interface evaluation

The teleoperator system man-machine interface evaluation develops and implements a program to determine human performance requirements in teleoperator systems

A hyper-redundant manipulator

“Hyper-redundant” manipulators have a very large number of actuatable degrees of freedom. The benefits of hyper-redundant robots include the ability to avoid obstacles, increased robustness with respect to mechanical failure, and the ability to perform new forms of robot locomotion and grasping. The authors examine hyper-redundant manipulator design criteria and the physical implementation of one particular design: a variable geometry truss

Lunar Rover with Multiple Science Handling Capability

A rover design study was undertaken for exploration of the Moon. Rovers that have been launched in the past carried a suite of science payload either onboard its body or on the robotic arm’s end. No rover has so far been launched and tasked with “carrying and deploying” a payload on an extraterrestrial surface. This paper describes a lunar rover designed for deploying payload as well as carrying a suite of instruments onboard for conventional science tasks. The main consideration during the rover design process was the usage of existing, in-house technology for development of some rover systems. The manipulation subsystem design was derived from the technology of Light Weight Robot, a dexterous arm originally developed for terrestrial applications. Recent efforts have led to definition of a mission architecture for exploration of the Moon with such a rover. An outline of its design, the manipulating arm technology and the design decisions that were made has been presented

The kinematics of hyper-redundant robot locomotion

This paper considers the kinematics of hyper-redundant (or “serpentine”) robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of “gaits”. The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length. We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot. Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions

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

Optimal Usage of Robot Manipulators

Robot-based automation has gained increasing deployment in industry. Typical application examples of industrial robots are material handling, machine tending, arc welding, spot welding, cutting, painting, and gluing. A robot task normally consists of a sequence of the robot tool center point (TCP) movements. The time duration during which the sequence of the TCP movements is completed is referred to as cycle time. Minimizing cycle time implies increasing the productivity, improving machine utilization, and thus making automationaffordable in applications for which throughput and cost effectiveness is of major concern.Considering the high number of task runs within a specific time span, for instance one year, the importance of reducing cycle time in a small amount such as a few percent will be more understandable.Robot manipulators can be expected to achieve a variety of optimum objectives. While the cycle time optimization is among the areas which have probably received the most attention so far, the other application aspects such as energy efficiency, lifetime of the manipulator, and even the environment aspect have also gained increasing focus. Also, in recent era virtual product development technology has been inevitably and enormously deployed toward achieving optimal solutions. For example, off-line programming of robotic workcells has become a valuable means for work-cell designers to investigate the manipulator’s workspace to achieve optimality in cycle time, energy consumption and manipulator lifetime.This chapter is devoted to introduce new approaches for optimal usage of robots. Section 2 is dedicated to the approaches resulted from translational and rotational repositioning of a robot path in its workspace based on response surface method to achieve optimal cycle time.Section 3 covers another proposed approach that uses a multi-objective optimization methodology, in which the position of task and the settings of drive-train components of a robot manipulator are optimized simultaneously to understand the trade-off among cycletime, lifetime of critical drive-train components, and energy efficiency. In both section 2 and 3, results of different case studies comprising several industrial robots performing different tasks are presented to evaluate the developed methodologies and algorithms. The chapter is concluded with evaluation of the current results and an outlook on future research topics on optimal usage of robot manipulators