Graphical Abstract Figure
Graphical Abstract Figure
Close modal

Abstract

This article presents a collision-free global path planning approach for multirobot systems called flexible formation—rapidly exploring randomized trees* (FF-RRT*). The algorithm is based on RRT* and provides a flexible and efficient representation of the formation geometry independent of the number of robots. It is designed to generate optimal paths for multirobot systems in a five-dimensional configuration space comprising the formation centroid, orientation, and scaling. An affine transformation is used to convert the path in the configuration space to the workspace of the robots. The proposed method employs a new distance function that eliminates the need for tuning weights and a sampling scheme for scaling factors to avoid inter-robot collisions. FF-RRT* is experimentally demonstrated using nonholonomic wheeled mobile robots and is effective in planning the collective motion of the robots. Robots could collectively reach goals by squeezing through narrow gaps and splitting and merging around large obstacles.

References

1.
Dorigo
,
M.
,
Theraulaz
,
G.
, and
Trianni
,
V.
,
2021
, “
Swarm Robotics: Past, Present, and Future [Point of View]
,”
Proc. IEEE
,
109
(
7
), pp.
1152
1165
.
2.
Shriyam
,
S.
,
Shah
,
B. C.
, and
Gupta
,
S. K.
,
2018
, “
Decomposition of Collaborative Surveillance Tasks for Execution in Marine Environments by a Team of Unmanned Surface Vehicles
,”
ASME J. Mech. Rob.
,
10
(
2
), p.
025007
.
3.
Poudel
,
L.
,
Elagandula
,
S.
,
Zhou
,
W.
, and
Sha
,
Z.
,
2023
, “
Decentralized and Centralized Planning for Multi-Robot Additive Manufacturing
,”
ASME J. Mech. Des.
,
145
(
1
), p.
012003
.
4.
Chiddarwar
,
S. S.
, and
Babu
,
N. R.
,
2011
, “
Conflict Free Coordinated Path Planning for Multiple Robots Using a Dynamic Path Modification Sequence
,”
Rob. Auton. Syst.
,
59
(
7–8
), pp.
508
518
.
5.
LaValle
,
S. M.
,
2006
,
Planning Algorithms
,
Cambridge University Press
,
Cambridge, UK
.
6.
Belta
,
C.
, and
Kumar
,
V.
,
2004
, “
Abstraction and Control for Groups of Robots
,”
IEEE Trans. Rob.
,
20
(
5
), pp.
865
875
.
7.
Borkar
,
A. V.
,
Borkar
,
V. S.
, and
Sinha
,
A.
,
2019
, “
Aerial Monitoring of Slow Moving Convoys Using Elliptical Orbits
,”
Eur. J. Control
,
46
, pp.
90
102
.
8.
Alonso-Mora
,
J.
,
Knepper
,
R.
,
Siegwart
,
R.
, and
Rus
,
D.
,
2015
, “
Local Motion Planning for Collaborative Multi-Robot Manipulation of Deformable Objects
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Seattle, WA
,
May 26–30
, IEEE, pp.
5495
5502
.
9.
Zhao
,
S.
,
2018
, “
Affine Formation Maneuver Control of Multiagent Systems
,”
IEEE. Trans. Automat. Contr.
,
63
(
12
), pp.
4140
4155
.
10.
Wallar
,
A.
, and
Plaku
,
E.
,
2014
, “
Path Planning for Swarms in Dynamic Environments by Combining Probabilistic Roadmaps and Potential Fields
,”
IEEE Symposium on Swarm Intelligence
,
Orlando, FL
,
Dec. 9–12
, IEEE, pp.
1
8
.
11.
Wang
,
D.
,
Zhang
,
J.
,
Jin
,
J.
,
Liu
,
D.
, and
Mao
,
X.
,
2021
, “
Rapid Global Path Planning Algorithm for Unmanned Surface Vehicles in Large-Scale and Multi-Island Marine Environments
,”
PeerJ Comput. Sci.
,
7
, p.
e612
.
12.
Chu
,
K.
,
Lee
,
M.
, and
Sunwoo
,
M.
,
2012
, “
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles
,”
IEEE Trans. Intell. Transp. Syst.
,
13
(
4
), pp.
1599
1616
.
13.
Roy
,
D.
,
Chowdhury
,
A.
,
Maitra
,
M.
, and
Bhattacharya
,
S.
,
2020
, “
Geometric Region-Based Swarm Robotics Path Planning in an Unknown Occluded Environment
,”
IEEE. Trans. Ind. Electron.
,
68
(
7
), pp.
6053
6063
.
14.
Borate
,
S. S.
, and
Vadali
,
M.
,
2021
, “
FF-RRT: A Sampling Based Path Planner for Flexible Multi-robot Formations
,”
In Proceedings of the 2021 5th International Conference of Advances in Robotics 2021
,
Kanpur, India
,
June 30
, pp.
1
6
.
15.
Båberg
,
F.
, and
Ögren
,
P.
,
2017
, “
Formation Obstacle Avoidance Using RRT and Constraint Based Programming
,”
IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)
,
Shanghai, China
,
Oct. 11–13
, IEEE, pp.
1
6
.
16.
Quan
,
L.
,
Yin
,
L.
,
Xu
,
C.
, and
Gao
,
F.
,
2022
, “
Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Philadelphia, PA
,
May 23–27
, IEEE, pp.
4979
4985
.
17.
Quan
,
L.
,
Yin
,
L.
,
Zhang
,
T.
,
Wang
,
M.
,
Wang
,
R.
,
Zhong
,
S.
,
Zhou
,
X.
,
Cao
,
Y.
,
Xu
,
C.
, and
Gao
,
F.
,
2023
, “
Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments
,”
IEEE Transact. Robotics
,
39
(
6
), pp.
4785
4804
.
18.
Kavraki
,
L. E.
,
Svestka
,
P.
,
Latombe
,
J. -C.
, and
Overmars
,
M. H.
,
1996
, “
Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces
,”
IEEE. Trans. Rob. Autom.
,
12
(
4
), pp.
566
580
.
19.
Geraerts
,
R.
, and
Overmars
,
M. H.
,
2004
, “A Comparative Study of Probabilistic Roadmap Planners,”
Algorithmic Foundations of Robotics V. Springer Tracts in Advanced Robotics
, Vol.
7
,
Springer
,
Berlin, Heidelberg
, pp.
43
57
.
20.
Karaman
,
S.
,
Walter
,
M. R.
,
Perez
,
A.
,
Frazzoli
,
E.
, and
Teller
,
S.
,
2011
, “
Anytime Motion Planning Using the RRT
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Shanghai, China
,
May 9–13
, IEEE, pp.
1478
1483
.
21.
Choset
,
H.
,
Lynch
,
K. M.
,
Hutchinson
,
S.
,
Kantor
,
G. A.
, and
Burgard
,
W.
,
2005
,
Principles of Robot Motion: Theory, Algorithms, and Implementations
,
MIT Press
,
Cambridge, MA
.
22.
Nitsche
,
M.
,
Krajnik
,
T.
,
Cizek
,
P.
,
Mejail
,
M.
, and
Duckett
,
T.
,
2015
, “
WhyCon: An Efficient, Marker-Based Localization System
,” https://robotica.dc.uba.ar/public/papers/nitsche2015.pdf, Accessed March 15, 2022.
You do not currently have access to this content.