The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

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

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.

https://www.mdpi.com/2079-9292/10/7/853/pdf

A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction

This paper proposes a novel mission planning platform, capable of efficiently deploying a team of UAVs to cover complex-shaped areas, in various remote sensing applications. Under the hood lies a novel optimization scheme for grid-based methods, utilizing Simulated Annealing algorithm, that significantly increases the achieved percentage of coverage and improves the qualitative features of the generated paths. Extensive simulated evaluation in comparison with a state-of-the-art alternative methodology, for coverage path planning (CPP) operations, establishes the performance gains in terms of achieved coverage and overall duration of the generated missions. On top of that, DARP algorithm is employed to allocate sub-tasks to each member of the swarm, taking into account each UAV's sensing and operational capabilities, their initial positions and any no-fly-zones possibly defined inside the operational area. This feature is of paramount importance in real-life applications, as it has the potential to achieve tremendous performance improvements in terms of time demanded to complete a mission, while at the same time it unlocks a wide new range of applications, that was previously not feasible due to the limited battery life of UAVs. In order to investigate the actual efficiency gains that are introduced by the multi-UAV utilization, a simulated study is performed as well. All of these capabilities are packed inside an end-to-end platform that eases the utilization of UAVs' swarms in remote sensing applications. Its versatility is demonstrated via two different real-life applications: (i) a photogrametry for precision agriculture and (ii) an indicative search and rescue for first responders missions, that were performed utilizing a swarm of commercial UAVs. The source code can be found at: https://github.com/savvas-ap/mCPP-optimized-DARP 

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Cooperative multi-UAV coverage mission planning platform for remote sensing applications

/pdf/cooperative-multi-uav-coverage-mission-planning-platform-for-2270lqqi.pdf

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC∗ for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm incorporates strict physical constraints like terrain traversability and material load capacity. We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and real field terrains in simulation environments. The experimental results show that our method significantly outperforms existing spiral-STC based mCPP methods. Our algorithm can find a set of well-balanced workload distributions for all robots and therefore, achieve the overall minimum time to complete the coverage.

MSTC ∗ :Multi-robot Coverage Path Planning under Physical Constrain

Many scholars have proposed different single-robot coverage path planning (SCPP) and multi-robot 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 article 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.

/pdf/a-multi-robot-coverage-path-planning-algorithm-for-the-paud12e4vw.pdf

Xiang Huang

Papers

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