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.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Miller, G. S. , 2002, “ 13 Snake Robots for Search and Rescue,” Neurotechnology for Biomimetic Robots, MIT Press, Cambridge, MA, p. 271.
Sreenivasan, S. , Goel, P. , and Ghosal, A. , 2010, “ A Real-Time Algorithm for Simulation of Flexible Objects and Hyper-Redundant Manipulators,” Mech. Mach. Theory, 45(3), pp. 454–466. [CrossRef]
Menon, M. S. , Ananthasuresh, G. K. , and Ghosal, A. , 2013, “ Natural Motion of One-Dimensional Flexible Objects Using Minimization Approaches,” Mech. Mach. Theory, 67, pp. 64–76. [CrossRef]
Hwang, Y. K. , and Ahuja, N. , 1992, “ Gross Motion Planning—A Survey,” ACM Comput. Surv., 24(3), pp. 219–291. [CrossRef]
Brooks, R. A. , 1983, “ Planning Collision-Free Motions for Pick-and-Place Operations,” Int. J. Rob. Res., 2(4), pp. 19–44. [CrossRef]
Lozano-Perez, T. , 1981, “ Automatic Planning of Manipulator Transfer Movements,” IEEE Trans. Syst., Man Cybern., 11(10), pp. 681–698. [CrossRef]
Fox, D. , Burgard, W. , and Thrun, S. , 1997, “ The Dynamic Window Approach to Collision Avoidance,” IEEE Rob. Autom. Mag., 4(1), pp. 23–33. [CrossRef]
Khatib, O. , 1986, “ Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” Int. J. Rob. Res., 5(1), pp. 90–98. [CrossRef]
Barraquand, J. , Langlois, B. , and Latombe, J.-C. , 1992, “ Numerical Potential Field Techniques for Robot Path Planning,” IEEE Trans. Syst., Man Cybern., 22(2), pp. 224–241. [CrossRef]
Rimon, E. , and Koditschek, D. E. , 1992, “ Exact Robot Navigation Using Artificial Potential Functions,” IEEE Trans. Rob. Autom., 8(5), pp. 501–518. [CrossRef]
Ó'Dúnlaing, C. P. , Sharir, M. , and Yap, C. K. , 1983, “ Retraction: A New Approach to Motion-Planning,” 15th Annual ACM Symposium on Theory of Computing, Boston, MA, Apr. 25–27, pp. 207–220.
Glasius, R. , Komoda, A. , and Gielen, S. C. A. M. , 1995, “ Neural Network Dynamics for Path Planning and Obstacle Avoidance,” Neural Networks, 8(1), pp. 125–133. [CrossRef]
Boyse, J. W. , 1979, “ Interference Detection Among Solids and Surfaces,” Commun. ACM, 22(1), pp. 3–9. [CrossRef]
Prescott, T. J. , and Mayhew, J. E. , 1991, “ Obstacle Avoidance Through Reinforcement Learning,” Neural Information Processing Systems (NIPS), Denver, CO, Dec. 2–5, pp. 523–530.
Suh, S.-H. , and Shin, K. G. , 1988, “ A Variational Dynamic Programming Approach to Robot-Path Planning With a Distance-Safety Criterion,” IEEE Trans. Rob. Autom., 4(3), pp. 334–349. [CrossRef]
Gilbert, E. G. , and Johnson, D. W. , 1985, “ Distance Functions and Their Application to Robot Path Planning in the Presence of Obstacles,” IEEE Trans. Rob. Autom., 1(1), pp. 21–30. [CrossRef]
Luh, J. Y. S. , and Lin, C. S. , 1981, “ Optimum Path Planning for Mechanical Manipulators,” ASME J. Dyn. Syst., Meas. Control, 103(2), pp. 142–151. [CrossRef]
Sundar, S. , and Shiller, Z. , 1997, “ Optimal Obstacle Avoidance Based on the Hamilton–Jacobi–Bellman Equation,” IEEE Trans. Rob. Autom., 13(2), pp. 305–310. [CrossRef]
Seshadri, C. , and Ghosh, A. , 1993, “ Optimum Path Planning for Robot Manipulators Amid Static and Dynamic Obstacles,” IEEE Trans. Syst., Man Cybern., 23(2), pp. 576–584. [CrossRef]
Hanafusa, H. , Yoshikawa, T. , and Nakamura, Y. , 1981, “ Analysis and Control of Articulated Robot With Redundancy,” 8th Trennial World Congress of IFAC, Kyoto, Japan, Vol. 4, pp. 1927–1932.
Maciejewski, A. A. , and Klein, C. A. , 1985, “ Obstacle Avoidance for Kinematically Redundant Manipulators in Dynamically Varying Environments,” Int. J. Rob. Res., 4(3), pp. 109–117. [CrossRef]
Lozano-Perez, T. , 1983, “ Spatial Planning: A Configuration Space Approach,” IEEE Trans. Comput., C-32(2), pp. 108–120. [CrossRef]
Branicky, M. S. , and Newman, W. S. , 1990, “ Rapid Computation of Configuration Space Obstacles,” IEEE International Conference on Robotics and Automation (ICRA), Cincinnati, OH, May 13–18, pp. 304–310.
Lozano-Perez, T. , 1987, “ A Simple Motion-Planning Algorithm for General Robot Manipulators,” IEEE Trans. Rob. Autom., 3(3), pp. 224–238. [CrossRef]
Chirikjian, G. S. , and Burdick, J. W. , 1992, “ A Geometric Approach to Hyper-Redundant Manipulator Obstacle Avoidance,” ASME J. Mech. Des., 114(4), pp. 580–585. [CrossRef]
Choset, H. , and Henning, W. , 1999, “ A Follow-the-Leader Approach to Serpentine Robot Motion Planning,” J. Aerosp. Eng., 12(2), pp. 65–73. [CrossRef]
Zhang, Y. , and Wang, J. , 2004, “ Obstacle Avoidance for Kinematically Redundant Manipulators Using a Dual Neural Network,” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., 34(1), pp. 752–759. [CrossRef]
Kahn, M. E. , and Roth, B. , 1971, “ The Near-Minimum-Time Control of Open-Loop Articulated Kinematic Chains,” ASME J. Dyn. Syst., Meas. Control, 93(3), pp. 164–172. [CrossRef]
Transeth, A. A. , Leine, R. I. , Glocker, C. , Pettersen, K. Y. , and Liljebäck, P. , 2008, “ Snake Robot Obstacle-Aided Locomotion: Modeling, Simulations, and Experiments,” IEEE Trans. Rob., 24(1), pp. 88–104. [CrossRef]
Liljebäck, P. , Pettersen, K. Y. , Stavdahl, Ø. , and Gravdahl, J. T. , 2014, “ A 3D Motion Planning Framework for Snake Robots,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, Sept. 14–18, pp. 1100–1107.
Pfotzer, L. , Staehler, M. , Hermann, A. , Roennau, A. , and Dillmann, R. , 2015, “ KAIRO 3: Moving Over Stairs & Unknown Obstacles With Reconfigurable Snake-Like Robots,” European Conference on Mobile Robots (ECMR), Lincoln, UK, Sept. 2–4, pp. 1–6.
Vonásek, V. , Saska, M. , Winkler, L. , and Přeučil, L. , 2015, “ High-Level Motion Planning for CPG-Driven Modular Robots,” Rob. Auton. Syst., 68(1), pp. 116–128. [CrossRef]
Reznik, D. , and Lumelsky, V. , 1994, “ Sensor-Based Motion Planning in Three Dimensions for a Highly Redundant Snake Robot,” Adv. Rob., 9(3), pp. 255–280. [CrossRef]
Weinstock, R. , 1952, Calculus of Variations: With Applications to Physics and Engineering, McGraw-Hill, New York.
Smith, D. , 1998, Variational Methods in Optimization, Dover Publications, Mineola, NY.
Steinhaus, H. , 1969, Mathematical Snapshots, Oxford University Press, New York.
Spanier, E. H. , 1994, Algebraic Topology, Vol. 55, Springer Science & Business Media, New York.
Gardner, M. , 1977, Mathematical Carnival: A New Round-Up of Tantalizers and Puzzles From Scientific American, Vintage Books, New York.
Barr, A. H. , 1981, “ Superquadrics and Angle-Preserving Transformations,” IEEE Comput. Graphics Appl., 1(1), pp. 11–23. [CrossRef]
MATLAB, 2012, “ MATLAB, Version 7.12.0 (R2011a),” The MathWorks, Inc., Natick, MA.


Grahic Jump Location
Fig. 1

Motion planning for a generic hyper-redundant robot

Grahic Jump Location
Fig. 2

Analytic 2D obstacles generated as superellipses

Grahic Jump Location
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

Grahic Jump Location
Fig. 4

Span of calculated link velocity solutions

Grahic Jump Location
Fig. 5

Flowchart of motion planning algorithm with obstacle avoidance

Grahic Jump Location
Fig. 6

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

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
Fig. 10

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

Grahic Jump Location
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

Grahic Jump Location
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



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In