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Research Papers

# A Decentralized, Communication-Free Force Distribution Method With Application to Collective Object Manipulation

[+] Author and Article Information

Mechanical Engineering Department,
Worcester Polytechnic Institute,
Worcester, MA 01609
e-mail: stasdighikalat@wpi.edu

Siamak G. Faal

Soft Robotics Laboratory,
Robotics Engineering,
Worcester Polytechnic Institute,
Worcester, MA 01609
e-mail: sghorbanifaal@wpi.edu

Cagdas D. Onal

Soft Robotics Laboratory,
Mechanical Engineering Department,
Worcester Polytechnic Institute,
Worcester, MA 01609
e-mail: cdonal@wpi.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received December 30, 2016; final manuscript received March 5, 2018; published online April 30, 2018. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 140(9), 091012 (Apr 30, 2018) (11 pages) Paper No: DS-16-1619; doi: 10.1115/1.4039669 History: Received December 30, 2016; Revised March 05, 2018

## Abstract

We present a novel approach to achieve decentralized distribution of forces in a multirobot system. In this approach, each robot in the group relies on the behavior of a cooperative virtual teammate that is defined independent of the population and formation of the real team. Consequently, such formulation eliminates the need for interagent communications or leader–follower architectures. In particular, effectiveness of the method is studied in a collective manipulation problem where the objective is to control the position and orientation of a body in time. To experimentally validate the performance of the proposed method, a new swarm agent, $Δρ$ (Delta-Rho), is introduced. A multirobot system, consisting of five $Δρ$ agents, is then utilized as the experimental setup. The obtained results are also compared with a norm-optimal centralized controller by quantitative metrics. Experimental results prove the performance of the algorithm in different tested scenarios and demonstrate a scalable, versatile, and robust system-level behavior.

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## References

Franks, N. R. , 1986, “Teams in Social Insects: Group Retrieval of Prey by Army Ants (Eciton burchelli, Hymenoptera: Formicidae),” Behav. Ecol. Sociobiol., 18(6), pp. 425–429.
Berman, S. , Lindsey, Q. , Sakar, M. S. , Kumar, V. , and Pratt, S. C. , 2011, “Experimental Study and Modeling of Group Retrieval in Ants as an Approach to Collective Transport in Swarm Robotic Systems,” Proc. IEEE, 99(9), pp. 1470–1481.
Krieger, M. J. , Billeter, J.-B. , and Keller, L. , 2000, “Ant-like Task Allocation and Recruitment in Cooperative Robots,” Nature, 406(6799), pp. 992–995. [PubMed]
Camazine, S. , 2003, Self-Organization in Biological Systems, Princeton University Press, Princeton, NJ.
Faal, S. G. , Kalat, S. T. , and Onal, C. D. , 2016, “Towards Collective Manipulation Without Inter-Agent Communication,” 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, Apr. 4–8, pp. 275–280.
Kube, C. R. , and Bonabeau, E. , 2000, “Cooperative Transport by Ants and Robots,” Rob. Auton. Syst., 30(1), pp. 85–101.
Wan, W. , Fukui, R. , Shimosaka, M. , Sato, T. , and Kuniyoshi, Y. , 2012, “Cooperative Manipulation With Least Number of Robots Via Robust Caging,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Kachsiung, Taiwan, July 11–14, pp. 896–903.
Kobayashi, Y. , and Hosoe, S. , 2012, “Cooperative Enclosing and Grasping of an Object by Decentralized Mobile Robots Using Local Observation,” Int. J. Soc. Rob., 4(1), pp. 19–32.
Campo, A. , Nouyan, S. , Birattari, M. , Groß, R. , and Dorigo, M. , 2006, “Negotiation of Goal Direction for Cooperative Transport,” Ant Colony Optimization and Swarm Intelligence, Springer, Berlin, pp. 191–202.
Rubenstein, M. , Cabrera, A. , Werfel, J. , Habibi, G. , McLurkin, J. , and Nagpal, R. , 2013, “Collective Transport of Complex Objects by Simple Robots: Theory and Experiments,” International Conference on Autonomous Agents and Multi-Agent Systems, St. Paul, MN, May 6–10, pp. 47–54.
Groß, R. , and Dorigo, M. , 2004, “Group Transport of an Object to a Target That Only Some Group Members May Sense,” Parallel Problem Solving From Nature (PPSN VIII), Birmingham, UK, Sept. 18–22, pp. 852–861.
Wang, Z. , and Schwager, M. , 2014, “Multi-Robot Manipulation Without Communication,” International Symposium on Distributed Autonomous Robotic Systems (DARS), pp. 135–149.
Wang, Z. , and Schwager, M. , 2016, “Kinematic Multi-Robot Manipulation With No Communication Using Force Feedback,” IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 16–21, pp. 427–432.
Kube, C. R. , and Zhang, H. , 1993, “Collective Robotics: From Social Insects to Robots,” Adapt. Behav., 2(2), pp. 189–218.
Zavlanos, M. M. , Jadbabaie, A. , and Pappas, G. J. , 2007, “Flocking While Preserving Network Connectivity,” 46th IEEE Conference on Decision and Control (CDC), New Orleans, LA, Dec. 12–14, pp. 2919–2924.
Becker, A. , Habibi, G. , Werfel, J. , Rubenstein, M. , and McLurkin, J. , 2013, “Massive Uniform Manipulation: Controlling Large Populations of Simple Robots With a Common Input Signal,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov. 3–7, pp. 520–527.
Craig, J. J. , 2005, Introduction to Robotics: Mechanics and Control, Vol. 3, Prentice Hall, Upper Saddle River, NJ. [PubMed] [PubMed]
Hogan, N. , 1984, “Impedance Control: An Approach to Manipulation,” American Control Conference (ACC), San Diego, CA, June 6–8, pp. 304–313.
Li, Z. , Hsu, P. , and Sastry, S. , 1989, “Grasping and Coordinated Manipulation by a Multifingered Robot Hand,” Int. J. Rob. Res., 8(4), pp. 33–50.
Golan, J. S. , 2012, “Moore–Penrose Pseudoinverses,” The Linear Algebra a Beginning Graduate Student Ought to Know, Springer, Dordrecht, The Netherlands, pp. 441–452.
Rubenstein, M. , Ahler, C. , and Nagpal, R. , 2012, “Kilobot: A Low Cost Scalable Robot System for Collective Behaviors,” IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, May 14–18, pp. 3293–3298.
Bonani, M. , Longchamp, V. , Magnenat, S. , Rétornaz, P. , Burnier, D. , Roulet, G. , Vaussard, F. , Bleuler, H. , and Mondada, F. , 2010, “The Marxbot, a Miniature Mobile Robot Opening New Perspectives for the Collective-Robotic Research,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct. 18–22, pp. 4187–4193.
Birkmeyer, P. , Peterson, K. , and Fearing, R. S. , 2009, “Dash: A Dynamic 16 g Hexapedal Robot,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, Oct. 10–15, pp. 2683–2689.
Kalat, S. T. , Faal, S. G. , Celik, U. , and Onal, C. D. , 2015, “Tribot: A Minimally-Actuated Accessible Holonomic Hexapedal Locomotion Platform,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, Sept. 28–Oct. 2, pp. 6292–6297.
Faal, S. G. , Chen, F. , Tao, W. , Agheli, M. , Tasdighikalat, S. , and Onal, C. D. , 2016, “Hierarchical Kinematic Design of Foldable Hexapedal Locomotion Platforms,” ASME J. Mech. Rob., 8(1), p. 011005.
Wang, Z. , Yang, G. , Su, X. , and Schwager, M. , 2016, “Ouijabots: Omnidirectional Robots for Cooperative Object Transport With Rotation Control Using No Communication,” International Conference Distributed Autonomous Robotics Systems, (DARS), London, Nov. 7–9, pp. 117–131.
Şahin, E. , 2004, “Swarm Robotics: From Sources of Inspiration to Domains of Application,” Swarm Robotics, Springer, Berlin, pp. 10–20.
Olariu, S. , and Zomaya, A. Y. , 2005, Handbook of Bioinspired Algorithms and Applications, CRC Press, Boca Raton, FL.
Velenis, E. , and Tsiotras, P. , 2005, “Optimal Velocity Profile Generation for Given Acceleration Limits: Theoretical Analysis,” American Control Conference, Vol. 2, p. 5.
Haddad, M. , Khalil, W. , and Lehtihet, H. , 2010, “Trajectory Planning of Unicycle Mobile Robots With a Trapezoidal-Velocity Constraint,” IEEE Trans. Rob., 26(5), pp. 954–962.
Neto, P. D. M. , Araújo, R. D. A. , Petry, G. G. , Ferreira, T. A. , and Vasconcelos, G. C. , 2007, “Hybrid Swarm System for Time Series Forecasting,” VI Encontro Nacional De Inteligência Artif. (ENIA), Uberlândia, Brazil.
Armstrong, J. S. , and Collopy, F. , 1992, “Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons,” Int. J. Forecasting, 8(1), pp. 69–80.
Willmott, C. J. , 1981, “On the Validation of Models,” Phys. Geogr., 2(2), pp. 184–194.
Hyndman, R. J. , and Koehler, A. B. , 2006, “Another Look at Measures of Forecast Accuracy,” Int. J. Forecasting, 22(4), pp. 679–688.
Habibi, G. , Schmidt, L. , Jellins, M. , and McLurkin, J. , 2016, “K-Redundant Trees for Safe and Efficient Multi-Robot Recovery in Complex Environments,” Robotics Research, Springer, Cham, Switzerland, pp. 149–165.
Kalat, S. T. , Faal, S. G. , and Onal, C. D. , 2017, “Scalable Collective Impedance Control of an Object Via a Decentralized Force Control Method,” American Control Conference (ACC), Seattle, WA, May 24–26, pp. 2680–2686.
Kalat, S. T. , 2017, “Virtual Coordination in Collective Object Manipulation,” Master's thesis, Worcester Polytechnic Institute, Worcester, MA.
Faal, S. G. , Kalat, S. T. , and Onal, C. D. , 2017, “Decentralized Obstacle Avoidance in Collective Object Manipulation,” NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Pasadena, CA, July 24–27, pp. 133–138.

## Figures

Fig. 1

(a) Δρ swarm agents and (b) a multirobot system consisting of five Δρ robots as they are manipulating a puzzle piece

Fig. 2

(a) System virtual configuration from each robots view. The virtual configuration for each robot consists of the robots itself and a virtual teammate located at its mirror location with respect to the object center of mass. (b) Utilization of the virtual agents eliminates the need for interagent communications and enables each robot to calculate its force based on the error vector of the object. The traction forces of the wheels are then computed using the Jacobian of the robotic platform, JA. (c) Graphical illustration of the experiment with 200 g payload. As observed from the figure, robots successfully move the object to the desired position and orientation as they are minimizing their attitude error ea with respect to the object.

Fig. 3

Description of the parameters used for derivation of the control equations for robots

Fig. 4

An overview of the experimental setup used for validation of the proposed algorithm

Fig. 5

Snapshots of five Δρ robots as they are manipulating a 100 g object. The manipulated object is assembled to a virtual puzzle piece depicted with white dashed line.

Fig. 6

Position and orientation of the object over time (vertical axis) for different group populations N. Path of the object center of mass is projected on x-y plane. Time responses for x and y are plotted in yt and xt planes, respectively. The dashed line on xt and yt planes illustrate the desired positions along x and y axes, respectively. The solid line represents the location of the goal over time on the xy plane. The orientation of the object over time is depicted via the black line parallel to the x axis of the object. The color gradient of the object, illustrates the passage of time.

Fig. 7

The x, y positions and the orientation of the object center of mass over time for (a) different payloads ranging from 100 g to 600 g and (b) different group formations. The solid line represents the desired object pose.

Fig. 9

Object velocity profile during the manipulation

Fig. 8

System configuration for robustness test. Initial robot positions around the object are represented using small circles. The desired pose of the object is depicted using dashed line. In each trial, different selections of the robots involved in the manipulation task are set to be inactive after 1 s, simulating partial power failure. The steady-state pose error values for each trial implies the ability of the team to accomplish the task despite failure of 40% of the team.

Fig. 10

Time-lapse figure of position and orientation of the object being moved along a sinusoidal trajectory. Path of the object center of mass is projected on xy plane in red. x and y time responses are plotted in xt and yt planes, respectively. The object color gradient, varying from cyan to magenta, illustrates the passage of time. Orientation of the object at each snapshot is depicted with a black line parallel to its x axis.

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