Research Papers

Trajectory Planning and Obstacle Avoidance for Hyper-Redundant Serial Robots

[+] Author and Article Information
Midhun S. Menon

Department of Mechanical Engineering,
Indian Institute of Science,
Bangalore 560 012, India
e-mail: midhun.sreekumar@gmail.com

V. C. Ravi

Centre for AI & Robotics,
Bangalore 560 093, India
e-mail: vc_ravi@cair.drdo.in

Ashitava Ghosal

Department of Mechanical Engineering,
Indian Institute of Science,
Bangalore 560 012, India
e-mail: asitava@mecheng.iisc.ernet.in

1Corresponding author.

Manuscript received July 22, 2016; final manuscript received March 25, 2017; published online May 15, 2017. Assoc. Editor: Satyandra K. Gupta.

J. Mechanisms Robotics 9(4), 041010 (May 15, 2017) (9 pages) Paper No: JMR-16-1208; doi: 10.1115/1.4036571 History: Received July 22, 2016; Revised March 25, 2017

Hyper-redundant snakelike serial robots are of great interest due to their application in search and rescue during disaster relief in highly cluttered environments and recently in the field of medical robotics. A key feature of these robots is the presence of a large number of redundant actuated joints and the associated well-known challenge of motion planning. This problem is even more acute in the presence of obstacles. Obstacle avoidance for point bodies, nonredundant serial robots with a few links and joints, and wheeled mobile robots has been extensively studied, and several mature implementations are available. However, obstacle avoidance for hyper-redundant snakelike robots and other extended articulated bodies is less studied and is still evolving. This paper presents a novel optimization algorithm, derived using calculus of variation, for the motion planning of a hyper-redundant robot where the motion of one end (head) is an arbitrary desired path. The algorithm computes the motion of all the joints in the hyper-redundant robot in a way such that all its links avoid all obstacles present in the environment. The algorithm is purely geometric in nature, and it is shown that the motion in free space and in the vicinity of obstacles appears to be more natural. The paper presents the general theoretical development and numerical simulations results. It also presents validating results from experiments with a 12-degree-of-freedom (DOF) planar hyper-redundant robot moving in a known obstacle field.

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Fig. 1

Motion planning for a generic hyper-redundant robot

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Fig. 2

Analytic 2D obstacles generated as superellipses

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Fig. 3

Superellipsoids ((a)–(c) are scaled uniformly and (d)–(f)are scaled nonuniformly): (a) (p1/q1)=1,(p2/q2)=1, (b) (p1/q1)=(1/5),(p2/q2)=1, (c) (p1/q1)=(1/5),(p2/q2)=(1/5), (d)p1/q1=1,p2/q2=1, (e) (p1/q1)=(1/3),(p2/q2)=1, and (f) (p1/q1)=1,(p2/q2)=1/7

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Fig. 4

Span of calculated link velocity solutions

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Fig. 5

Flowchart of motion planning algorithm with obstacle avoidance

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Fig. 6

Trajectory for 2D simulation with snapshot locations and initial configuration of the robot

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Fig. 7

Motion snapshots for 2D simulation: (a) snapshot 1, (b) snapshot 2, (c) snapshot 3, (d) snapshot 4, (e) snapshot 5, and (f) snapshot 6

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Fig. 8

Simulation parameter plots: (a) plot of head, tail, and intermediate joint angles and (b) plot of Lagrange multipliers for first elliptical obstacle for all links

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Fig. 9

Motion snapshots for 3D simulation: (a) trajectory with snapshot locations and initial configuration, (b) snapshot at 1, (c) snapshot at 2, (d) snapshot at 3, (e) snapshot at 4, (f) snapshot at 5, (g) snapshot at 6, and (h) snapshot at 7

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Fig. 10

Experimental prototype: (a) experimental 12DOF hyper-redundant robot and (b) close-up of joint and wheel assembly of the experimental robot

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Fig. 11

Experimental setup and results: (a) workspace with obstacles and desired path of the robot head and (b) comparison of joint angles at various points in the body

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Fig. 12

Simulated (ac) and actual (de) configuration of the robot at steps 45, 65, and 97 of the motion: (a,d) step 45, (b,e) step 65, and (c,f) step 97




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