Citation: Yu, L.; Wang, Z.
Multi-Robot Robust Motion Planning
based on Model Predictive Priority
Contouring Control with
Double-Layer Corridors. Appl. Sci.
2022, 12, 1682. https://doi.org/
10.3390/app12031682
Academic Editors: Dario Richiedei
and Luigi Fortuna
Received: 2 December 2021
Accepted: 3 February 2022
Published: 6 February 2022
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Article
Multi-Robot Robust Motion Planning based on Model
Predictive Priority Contouring Control with
Double-Layer Corridors
Lingli Yu * and Zhengjiu Wang
School of Automation, Central South University, Changsha 410083, China; zheng19jiu_wang@csu.edu.cn
* Correspondence: llyu@csu.edu.cn; Tel.: +86-138-7318-3564
Featured Application: This work is used for multi-robot autonomous systems.
Abstract:
Disturbance poses a major challenge for the safety and real-time performance of robust
robot motion planning. To address the disturbance while improving the real-time performance
of multi-robot robust motion planning, a model predictive priority contouring control method is
proposed. First, an improved conflict-based search (ICBS) planner is utilized to plan reference paths.
The low-level planner of the conflicted-based search (CBS) planner is replaced by the hybrid A*
planner and reference paths are adopted as an initial guess of model predictive priority contouring
control. Second, double-layer corridors are proposed to provide safety guarantees, which include
static-layer corridors and dynamic-layer corridors. The static-layer corridors are generated based on
reference paths and the dynamic-layer corridors are generated based on the relative positions and
velocities of robots. The double-layer corridors are applied as safety constraints of model predictive
priority contouring control. Third, a prioritization mechanism is devised to improve computational
efficiency. Priorities are assigned according to each robot’s task completion percentage. Based on
the assigned priority, multiple robots are grouped, and each group executes the model predictive
priority contouring control algorithm to acquire trajectories. Finally, our method is compared with
the centralized method and the soft constraint-based DMPC. Simulations verify the effectiveness and
real-time performance of our approach.
Keywords:
multi-robot system; robust motion planning; model predictive priority contouring control;
double-layer corridor
1. Introduction
Multi-robot systems play a vital role in next-generation factories, urban search and res-
cue, and package delivery, and they are anticipated to be applied in space
exploration [1,2].
One of the key ingredients to a multi-robot system is the motion planning module. The
motion planning module should find a set sequence of valid configurations that move
multiple robots from starting points to goal points safely [
3
]. However, in unstructured
environments, the multi-robot motion planning algorithm being both optimal, complete,
real-time, and flexible is still a valuable and challenging problem to be solved, especially in
the presence of disturbances.
As mentioned in previous publications, the multi-robot motion planning (MMP)
problem is generally defined as a multi-objective optimization problem with different types
of constraints [
4
,
5
]. The methods for solving MMP problems are mainly classified into
centralized and decentralized methods [3].
Centralized methods plan the motion trajectories of robots jointly [
6
]. In [
7
], Guni
Sharon et al. proposed a two-level algorithm called conflict-based search (CBS) to find
optimal paths for multiple robots. The core of the algorithm is to maintain a constraint tree
to resolve conflicts and plan the optimal path for each robot with constraints. In [
8
], Jiaoyang
Appl. Sci. 2022, 12, 1682. https://doi.org/10.3390/app12031682 https://www.mdpi.com/journal/applsci
