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Research Papers

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

[+] Author and Article Information
Audelia G. Dharmawan

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372
e-mail: audelia@sutd.edu.sg

Shaohui Foong

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372
e-mail: foongshaohui@sutd.edu.sg

Gim Song Soh

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372
e-mail: sohgimsong@sutd.edu.sg

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received June 13, 2017; final manuscript received January 15, 2018; published online April 5, 2018. Assoc. Editor: K. H. Low.

J. Mechanisms Robotics 10(3), 031010 (Apr 05, 2018) (10 pages) Paper No: JMR-17-1178; doi: 10.1115/1.4039395 History: Received June 13, 2017; Revised January 15, 2018

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

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Copyright © 2018 by ASME
Topics: Robots , Path planning
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Figures

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Fig. 1

The redundant robot in consideration is an omnidirectional mobile platform equipped with an industrial robot arm

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Fig. 2

Illustration of the homotopy motion planning

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Fig. 4

Motion planning results for two types of redundant robots with obstacles along the optimal mobile robot path: (a) mobile manipulator with nonspherical wrist and (b) mobile manipulator with spherical wrist

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Fig. 5

Simulated optimal motion of the robots: (a) mobile manipulator with nonspherical wrist and (b) mobile manipulator with spherical wrist

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Fig. 6

A snapshot of the ROS RViz implementation

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Fig. 7

Comparison of results from two different solvers. Note that ga solver in matlab is unable to find the solution.

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Fig. 3

The task constraints in consideration is the welding of pipe structures for jack up oil rig manufacturing

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Fig. 8

Comparison between various optimization solvers and motion planning approaches in terms of the average computational time per unit task length per robot's segment involved in collision check

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Fig. 10

ABB IRB1410 robot

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