Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation
Authors: Duy Anh Nguyen, Nhat Minh Dinh Le, Nhan Huu Nguyen, Pham Duy Hung, Van Anh Ho, Trung Dung Ngo
Organizations: More-Than-One Robotics Laboratory, Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, Canada · Soft Haptics Lab., School of Materials Science, Japan Advanced Institute of Science and Technology, Asahidai 1-1, Nomi, Ishikawa, 923-1292 Japan. · University of Engineering and Technology, Vietnam. · Japan Science and Technology Agency, PRESTO, Kawaguchi, Saitama, 332-0012 Japan.
In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired moving target without their inter-robot communication. These mobile robots are equipped with 360-degree vision-based soft tactile sensors utilized to determine contact location and resultant impact force. By investigating the dynamics of moving rigid objects on the flat, we proposed a distributed cascade control. The inner loop control incorporates contact force and positioning, ensuring the robots' pushing contact and application of the desired force to the object. The outer loop control coordinates the robots to push the object in a desired direction without inter-robot communication, regardless of the unknown object mass. The stability and convergence of the system are verified using the Lyapunov stability theory. We also conducted simulation and real-word experiments to validate the performance of the proposed control method, and the experimental results showcase the successful coordination of multiple robots in pushing an object towards a moving desired direction.
Figures & tables
Fig. 1 : Real-world experiment (top) and simulation (bottom) of multiple soft-tactile-based mobile robots performing an object transportation task.
Fig. 2 : Free-body diagram of an object under a pushing force fi .
Fig. 3 : The Visual-based tactile model with contact angle and contact force.
Fig. 4 : Distributed Cascade control (DCFC) graph including an inner loop to maintain pushing contact and force, and an outer loop to align force vector to the desired direction.
Parameters
m
I
μ0,t
μ1,t
μ0,r
μ1,r
Scenario 1
0.67 kg
1 kgm2
0.25
5
1
1
TABLE I : Object and environment Parameters
Robot Para.
n1
n2
k
fmax
−
−
−
Values
1
1
400
4N
-
-
-
Control Para.
f0
KPo
KIo
KDo
Kω
KP,in
KI,in
Values
1.5N
3
0.5
0
10
20
4
TABLE II : Robot and Control Parameters
Fig. 5 : Simulation results demonstrating the convergence to zero.
Fig. 6 : Real-world experiment results for case 1 with constant desired moving direction.
Fig. 7 : Real-world experiment results for case 2 with changing desired moving direction.
More-Than-One Robotics Laboratory, Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, Canada · Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology, Hanoi, Vietnam
Humanoid Sensing and Perception, Istituto Italiano di Tecnologia, Genoa, Italy. · DIBRIS, Universit`a di Genova, Via All’Opera Pia, 13, Genoa, Italy. · MESH Facility, Istituto Italiano di Tecnologia, Genoa, Italy
Center for Artificial Intelligence and Robotics, Indian Institute of Technology Mandi, India · Department of Mechanical Engineering, Clemson University, Clemson, SC 29630, USA