Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree

Abstract

The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

“…Chen et al [13] improved the accuracy of the spraying path by using an exponential mean Bézier curve and trajectory optimization based on ACO or GA, further enhancing the smooth path by optimizing the trajectory on the Bézier surface [241]. Gao et al [242] proposed an improved ACO algorithm to optimize the coverage performance by reducing the number of turns in multi-robot CPP in simulated 2D grid space. Ye et al [12] improved the algorithm by randomly calculating the transition probability and updating the pheromone besides the acceleration factor, improving the global searchability despite the randomness of the algorithm could induce failure.…”

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

“…Similarly, Bouzid et al 23 applied rapidly exploring random tree star fixed nodes (RRT*-FN) combined with the genetic algorithm (GA) by assuming that the CPP issue emulates a vehicle routing problem (VRP). In addition, Gao et al 24 used spanning tree coverage (STC) with ant colony optimization (ACO). On the other hand, Ju et al 25 applied a distributed swarm control algorithm to show that the performance of the multi-UAV system is significantly superior to the single-UAV system.…”

Distributed coverage optimisation for a fleet of unmanned maritime systems

“…If continuous broadband access between the agents is assumed, then all agents can obtain perfect localisation and sensor data from one another, and then the approaches can be based on some kind of global optimisation approach [12], with the capability to adapt to a timedependent environment [13]. Even though it has been shown that finding a globally optimal solution for the coverage maximisation of a multi-agent fleet is an NP-hard problem [14], it is possible to come quite close to this solution within real-time constraints [15], [16]; however, this requires intelligent strategies to guide the optimisation process (see more discussion on this subject later).…”