A Multi-Robot Coverage Path Planning Algorithm for the Environment With Multiple Land Cover Types

Abstract

Many scholars have proposed different single-robot coverage path planning (SCPP) and multirobot coverage path planning (MCPP) algorithms to solve the coverage path planning (CPP) problem of robots in specific areas. However, in outdoor environments, especially in emergency search and rescue tasks, complex geographic environments reduce the task execution efficiency of robots. Existing CPP algorithms have hardly considered environmental complexity. This paper proposed an MCPP algorithm considering the complex land cover types in outdoor environments to solve the related problems. The algorithm first describes the visual fields of the robots in different land cover types by constructing a hierarchical quadtree and builds the adjacent topological relations among the cells in the same and different layers in the hierarchical quadtree by defining shared neighbor direction based on Binary System. The algorithm then performs an approximately balanced task assignment to the robots considering the moving speeds in different land cover types using the azimuth trend method we proposed to ensure the convergence of the task assignment process. Finally, the algorithm improves Spanning Tree Covering (STC) algorithm to complete the CPP in the area where each robot belongs. This study used a classification image of the real outdoor environment to the verification of the algorithm. Results show that the coverage paths planned by the algorithm are reasonable and efficient and its performance has obvious advantages compare with the current mainstream MCPP algorithm. INDEX TERMS Coverage path planning algorithm, complex geographic environments, emergency search, multi-robot CPP, multiple land cover types, terrain subdivision This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

“…In CPP, MCPP is considered a challenge [18]. However, compared to single-robot CPP, MCPP has several advantages, including reduced time consumption and improved execution efficiency by completing tasks in parallel [6]. In addition, if some members of the robot team fail, other robots can compensate for the problem [13], which improves the system's robustness.…”

“…The essence of the multi-robot surveillance system (MRSS) is for multiple robots to completely cover a given area within a limited time. This can be called multi-robot coverage path planning (MCPP) in the MRSS [6]. MCPP is a special case of coverage path planning (CPP) that involves generating a path for a robot or multiple robots that covers the entire area while minimizing the time required to complete the coverage task [7].…”

Cooperative Multi-UAV Coverage Mission Planning Platform for Remote Sensing Applications

“…This family of approaches is the most commonly used for real-life operations, at the moment, because of its incorporated advantages and its out-of-thebox adaptability to complex-shaped ROIs. However, it is not the most energy-efficient one, as it many times includes redundant movements that do not contribute to the scanning procedure (e.g., from the starting/ending points to the home position of the vehicles) and multiple [22] Exclusive & Proportional Convex polygons [14], [23] -Grid with obstacle cells obstacle cells [24], [25] Single path allocation based on initial positions Grid with obstacle cells obstacle cells [26] Non-exclusive single path allocation Grid with obstacle cells obstacle cells [27] Exclusive Equal Grid with obstacle cells obstacle cells [28] Exclusive & Equal Multi-scale grid with obstacles obstacle cells [ **In the paper NFZs are only included between the home position of the UAVs and the first mission's waypoint, but not inside the ROI.…”

“…Based on a different approach, where an overall region is divided in exclusive sub-regions and then allocated to a group of UAVs, [28] identifies the gap of coverage path planning in complex geographic environments and proposes an STC-based mCPP method, called MCCP-MLCT. This method constructs a grid with multiple scales (different sizes of cells), based on the complexity and topology of the environment that will be applied.…”

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

“…Still, it cannot deal with the pathway through the free sub-cells situation in which the cells are occupied by obstacles, especially in robot placement along the same axis. In [74], the workspace is divided into different cell sizes based on the hierarchical quadtree structure, following the construction of the spanning tree by considering different edge lengths. This method could minimize the repeated coverage and balance the task assignment, but introduces over-segmentation in the cell, leading to extra task costs.…”