Research Papers

Modular Manipulators for Cluttered Environments: A Task-Based Configuration Design Approach

[+] Author and Article Information
Satwinder Singh

Clinical Affairs Department,
Bio-Medical Engineering (HK) Ltd.,
Cyberport, Hong Kong
e-mail: satwindersn@iitrpr.ac.in

Ashish Singla

Department of Mechanical Engineering,
Thapar Institute of Engineering and Technology,
Patiala 147004, India
e-mail: ashish.singla@thapar.edu

Ekta Singla

Mechanical Engineering Department,
Indian Institute of Technology Ropar,
Rupnagar 140001, India
e-mail: ekta@iitrpr.ac.in

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received December 9, 2017; final manuscript received June 8, 2018; published online July 18, 2018. Assoc. Editor: Pierre M. Larochelle.

J. Mechanisms Robotics 10(5), 051010 (Jul 18, 2018) (11 pages) Paper No: JMR-17-1410; doi: 10.1115/1.4040633 History: Received December 09, 2017; Revised June 08, 2018

Modular manipulators gained popularity for their implicit feature of “reconfigurability”—that is, the ability to serve multiple applications by adopting different configurations. As reported in the literature, most of the robotic arms with modular architecture used specific values of twist angles, e.g., 0 deg or 90 deg. Further, the number of degrees-of-freedom (DoF) is also kept fixed. These constraints on the design parameters lead to a smaller solution space for the configuration synthesis problems and may result as no-feasible solution in a cluttered work-cell. To work in a realistic environment, the task-based customized design of a manipulator may need a larger solution space. This work deals with the extension of the modular architecture from conventional values to unconventional values of design parameters, keeping the degrees-of-freedom also as variable. This results into an effective utilization of modular designs for highly cluttered environments. A three-phase design strategy is proposed in the current work. The design strategy starts with the decision of optimal number of modules required for the given environment in the first phase, which is followed by task-based “configuration planning” and “optimal assembly” in the second and third phase, respectively. Three types of modules are proposed with same architecture and different sizes—heavy (H), medium (M), and light (L). The configuration planning includes detailed discussion on the type-selection of the modules and their possible combinations. Comparison of all possible n-link combinations is analyzed based upon the optimized results with respect to the minimum torque values. Case studies of a power plant with two different workspaces are included to illustrate the three-phase strategy representing the importance of modularity in nonrepetitive maintenance tasks.

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Grahic Jump Location
Fig. 1

Interconnectivity of different sections of the modular design strategy

Grahic Jump Location
Fig. 2

Twist adjustment unit and basic length casing

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Fig. 3

Twist angle adjustment between two basic link units

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Fig. 4

Comparison of performance measure values for same manipulator assembly working in different environments

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Fig. 5

Complete structure: Optimal assembly selection

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Fig. 6

Comparison of all possible six-link assembly combinations: Case study 1 of server room

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Fig. 7

Case study of a server room environment: The cluttered environment, 3 TSLs, the resulting modular assembly, and corresponding skeletal view of the manipulator working in the workcell: (a) solid model of a server room environment, (b) skeletal view of server room environment with TSLs, (c) modular library, (d) modular assemblies having minimum and maximum worst joint torque, (e) skeletal view six-link manipulator, and (f) optimal assembly H1M1L4

Grahic Jump Location
Fig. 8

A simplified power plant environment to simulate the obstacles

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Fig. 9

Two different sections of a power plant environment

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Fig. 10

Case study of a power plant environment: The environment with 5 TSLs, the resulting optimal assembly, and the corresponding skeletal view of the manipulator in the workcell: (a) power plant environment with five TSLs, (b) skeletal view of eight-link configurations corresponding to each given task locations, (c) optimal assembly H3M2L3, and (d) manipulator using a movable platform on rails

Grahic Jump Location
Fig. 11

Comparison of all feasible eight-link assembly combinations: Case study 2 of a power plant (section 1)

Grahic Jump Location
Fig. 12

Comparison of all feasible seven-link assembly combinations: Case study 3 of a power plant (section 2)

Grahic Jump Location
Fig. 13

Case study of a power plant environment: (a) power plant environment with 4 TSLs, (b) the skeletal view of the resulting manipulator in the workcell, and (c) path planner utilized to ensure possible connectivity between two TSLs



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