Lounell B. Gueta

Coordinated motion control of a robot arm and a positioning table with arrangement of multiple goals

The minimum-time motion coordination is an important subject in robotics. In this study, the arrangement of several goals, which is treated as a traveling salesman problem (TSP), is incorporated to this subject. Although TSP has been studied in most previous works; but, solving a TSP that takes into account collision occurrences has not received much attention. This instance arises when a robot arm has to plan a sequence of reaching goals with other moving objects and/or other robot arms. If goals are also moving, then the problem becomes more complex since the end configuration of robot arm is undefined when reaching a goal. In this study, in particular, a 6-DOF robot arm has to reach several goals found in an object while a 1-axis positioning table simultaneously positions the object; thereby changing the goal locations and collision occurrences are inevitable. For the purpose of this study, the TSP is solved effectively with motion coordination and collision avoidance. The collision-free configurations of a robot arm when reaching goals are solved through motion coordination. Collision is avoided by exploiting the redundancy of the system. The above-mentioned solution is verified through a simulation utilizing an object with various numbers of goals and their positions, and is proven effective.

Practical Point-to-Point Multiple-Goal Task Realization in a Robot Arm with a Rotating Table

This study presents a multiple-goal task realization in a system composed of a 6-d.o.f. robot arm and a one-axis rotating table. The problem is complex due to the existence of multiple goals and the kinematic redundancy of the system. We propose a design approach integrating the base placement, task sequencing and motion coordination methods. We show that this approach reduces the task completion time of the robot arm; the motion planning is realized through straight-line paths in the configuration space despite collision occurrences. Furthermore, we introduce a hybrid graph-search method combining the greedy nearest-neighbor method and the Dijkstra method to solve the motion coordination of the robot arm and the table. We show the effectiveness of the design approach and the search method through a time-constrained simulation-based optimization.

Compact design of work cell with robot arm and positioning table under a task completion time constraint

A work cell is generally designed to achieve a high throughput and its size is typically viewed as contingent to component sizes. In this paper, we aim to design a compact work cell (spatial requirement) and to minimize its task completion time (temporal requirement) to a value set as a constraint. By doing so, a work cell occupies a minimal space and achieves its desired throughput. The work cell size is evaluated based on the size and the swept volume of components. This evaluation is important since a robot arm can have a very large swept volume depending on a given task. To satisfy the spatial and temporal requirements, we propose the integration of the base placement optimization, goal rearrangement, and motion coordination between the robot arm and the positioning table. Furthermore, we introduce two motion coordination schemes based on the spatial and temporal requirements. We showed the effectiveness of the proposed method through simulations.

Selection of manipulator system for multiple-goal task by evaluating task completion time and cost with computational time constraints

The focus of this study is on the problem of manipulator system selection for a multiple-goal task by evaluating task completion time and cost with computational time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of tasks and the result shows the effectiveness of the proposed approach.

Design of the end-effector tool attachment for robot arm with multiple reconfigurable goals

A custom-made robot arm that is specially designed for a given task is better than a general-purpose robot arm based on the performance index it is designed for. In industries, however, a general-purpose robot arm is prevalently used since it can perform over several and varying tasks. Because of this, a custom-made robot arm becomes impractical due to its high fabrication cost. In this study, we propose the design of tool attachment as a cost-effective and alternative method for improving the performance of a general-purpose robot arm. Wherein, the task completion time is the performance index in designing the tool attachment. A tool attachment, a passive linkage attached between the end-effector of robot arm and tool, is customized for a given task. We showed that the proposed method is effective by employing it in a task with multiple reconfigurable goals, or goals that can be rearranged and can be positioned by a table.

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.

Multiple-goal task realization utilizing redundant degrees of freedom of task and tool attachment optimization

Minimizing the task completion time of manipulator systems is essential in order to achieve high productivity. In this paper, this problem is dealt with by utilizing the redundant degrees of freedom (DOF) of a given task and the tool attachment optimization. For example, in a vision-based inspection where a camera is held by a manipulator, the extra DOF can be brought about by allowing the camera to be translated along its approach axis or rotated about this axis when capturing images. Furthermore, the manipulator end-effector position and orientation is optimized by designing an additional linkage at the manipulator end-effector which is called a tool attachment. A 7-DOF manipulator system is used in the simulations to verify the proposed approach. Results showed that this approach can minimize the task completion time by about 17% compared to conducting only motion coordination.

A practical and integrated method to optimize a manipulator-based inspection system

A method to minimize the working time of a 6-DOF robot arm in inspecting products is presented. It involves subdividing one large complex problem into several sub- problems. There are three issues considered: (1) the base position of robot arm (2) the order of product parts to be inspected called inspection points and (3) the heat generated in joint motors. The proposed method used the Tabu search (TS) to find optimal base position and the Lin-Kernighan (LK) method to optimize the order of inspection points. The end-effector trajectory is based on trapezoidal velocity method (TVP) with heat constraint, which prevents overheating the robot arm joints. The proposed method is proved effective through simulation and is useful for actual application in manufacturing industries.

Manipulator system selection based on evaluation of task completion time and cost

Task completion time and cost are two significant criteria for the selection of manipulator system. For a given task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation time. In this paper, we propose a method that can select appropriate systems by evaluating task completion time and cost within the desired calculation time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the task completion time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of tasks and is proved to be effective and practical.

Rearrangement task of multiple robots using task assignment applicable to different environments

This paper addresses a rearrangement problem by a group of mobile robots and proposes a method for task assignment and path planning applicable to different kinds of environment. The method minimizes task completion time considering complexity of robots paths in achieving the goal state of a working environment. It minimizes the number of possible delivery tasks between robots thereby reducing the task completion time. The proposed method is compared with continuous transportation method and Territorial Approach through simulations. The results of simulations show the effectiveness of the proposed method. The proposed method can realize an efficient rearrangement task by mobile robots in various working environments under feasible computation time.