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Online On-Demand Multi-Robot Coverage Path Planning

Ratijit Mitra, Indranil Saha

TL;DR

Experimental results show that the algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage.

Abstract

We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed previously computed paths entirely (thus on-demand) and reuses the remaining paths for the other robots. We formally prove that the algorithm guarantees complete coverage of the unknown workspace. Experimental results on several standard benchmark workspaces show that our algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage. For its validation, we perform ROS+Gazebo simulations in five 2D grid benchmark workspaces with 10 Quadcopters and 10 TurtleBots, respectively. Also, to demonstrate its practical feasibility, we conduct one indoor experiment with two real TurtleBot2 robots and one outdoor experiment with three real Quadcopters.

Online On-Demand Multi-Robot Coverage Path Planning

TL;DR

Experimental results show that the algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage.

Abstract

We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed previously computed paths entirely (thus on-demand) and reuses the remaining paths for the other robots. We formally prove that the algorithm guarantees complete coverage of the unknown workspace. Experimental results on several standard benchmark workspaces show that our algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage. For its validation, we perform ROS+Gazebo simulations in five 2D grid benchmark workspaces with 10 Quadcopters and 10 TurtleBots, respectively. Also, to demonstrate its practical feasibility, we conduct one indoor experiment with two real TurtleBot2 robots and one outdoor experiment with three real Quadcopters.
Paper Structure (29 sections, 8 theorems, 1 equation, 10 figures, 1 table, 4 algorithms)

This paper contains 29 sections, 8 theorems, 1 equation, 10 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Problem
  3. Preliminaries
  4. Workspace
  5. Robots and their States
  6. Motions and Paths of the robots
  7. Problem Definition
  8. On-Demand CPP Framework
  9. Data structures at CP
  10. Overall On-Demand Coverage Path Planning
  11. Coverage Path Planning in the Current Horizon
  12. Coverage Path Planning for the Participants
  13. Sum-of-Costs-optimal goal assignment and corresponding paths
  14. Prioritization
  15. Time parameterization
  16. ...and 14 more sections

Key Result

Lemma 1

ensures that at least one goal gets visited in each horizon.

Figures (10)

  • Figure 1: Overview of on-demand CPP
  • Figure 2: Incremental updation of the global view $W$
  • Figure 3: Skip replanning the participants in the current horizon
  • Figure 4: Cost-optimal goal assignment and optimal paths
  • Figure 5: Infeasible paths
  • ...and 5 more figures

Theorems & Definitions (26)

  • Example 1
  • Example 2
  • Example 3
  • Example 4
  • Example 5
  • Example 6
  • Example 7
  • Example 8
  • Example 9
  • Example 10
  • ...and 16 more