Research Papers

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

[+] Author and Article Information
Shadi Tasdighi Kalat

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

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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Grahic Jump Location
Fig. 1

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

Grahic Jump Location
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.

Grahic Jump Location
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.

Grahic Jump Location
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.

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
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.

Grahic Jump Location
Fig. 9

Object velocity profile during the manipulation

Grahic Jump Location
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.

Grahic Jump Location
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.



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