R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Tang, Gang; Tang, Congqiang; Zhou, Hao; Claramunt, Christophe; Men, Shaoyang

doi:10.3390/rs13081525

Cited by 22 publications

(24 citation statements)

References 35 publications

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“…2 and Tab. 4 In this part, we verify the effectiveness of the new algorithm PESSA through 10 benchmark functions (Table 1), and compare it with 6 algorithms to more fully illustrate its superiority. Figure 5 and Figure 6 show the convergence process of four unimodal benchmark functions and six multimodal benchmark functions respectively.…”

Section: Experimental Analysis Of the Algorithmmentioning

confidence: 99%

“…In order to improve the stability and efficiency of path planning methods in complex environments, the literature [3] presented a RETRBG algorithm, a robust and efficient trajectory replanning method based on the guiding path. The literature [4] introduced CPP method based on a Region Optimal Decomposition (ROD), which achieved good results when applied to the path planning of UAV in a port environment. The literature [5] proposed a new diving planning method based on the original underwater pathfinding algorithm, which considers safety constraints and maximizes the number of points of interest (POIs) visited.…”

Section: Introductionmentioning

confidence: 99%

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A parallel particle swarm optimization and enhanced sparrow search algorithm for unmanned aerial vehicle path planning

Wang

2022

Preprint

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The path planning problem is that Unmanned aerial vehicle (UAV) needs to plan a feasible path from the starting point to the end point in a specific environment. But it cannot get satisfactory results using ordinary algorithms, to solve this problem, a hybrid algorithm named as PESSA is proposed, where particle swarm optimization (PSO) and an enhanced sparrow search algorithm (ESSA) work in parallel. In the ESSA, the random jump of the producer’s position is strengthened to guarantee the global search ability, each scrounger keeps learning from the optimal experience of the producers, and for the sparrow with the optimal position, when it perceives the danger, the difference between the best individual and the worst individual will be imposed to speed up the search process. And then, the elite reverse search strategy was added to increase the diversity of the population. In this paper, the performance of PESSA is verified by 10 basic functions, the experimental results illustrate that PESSA is better compared with other twelve algorithms. Finally, the proposed PESSA is applied in 4 different scenarios including two groups of 2D environments and two groups of 3D environments. The results show that the PESSA algorithm can acquire more feasible and effective route than compared algorithms.

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Section: Experimental Analysis Of the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A parallel particle swarm optimization and enhanced sparrow search algorithm for unmanned aerial vehicle path planning

Wang

2022

Preprint

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“…To this day, coverage path planning remains an active area of research as it is applicable for unmanned aerial vehicles (UAVs) [6], autonomous underwater vehicles (AUVs), unmanned ground vehicles (UGVs), autonomous surface vehicles (ASVs) and also planning the path of the end-effector of industrial robots that need to perform surface treatment operations such as milling or laser cleaning. The authors of [15] present a comprehensive review of CPP in robotics using classical and heuristic algorithms; although no specific mention is made of the particular case of sweeping robots, some references are made to general cleaning robots from which it can be identified that reported research is focused on solving the problem of single areas with obstacles [8,[16][17][18][19][20][21] or conjoined areas within a delimited squared space [5,[22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…Single regions are defined by one connected component, convex or non-convex, and with or without obstacles in the interior of the connected component. Some examples are [4][5][6][7][8]. Disjoint regions present areas with more than one component.…”

Section: Introductionmentioning

confidence: 99%

A Divide and Conquer Strategy for Sweeping Coverage Path Planning

Vasquez-Gomez

Merchán-Cruz

2022

Energies

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One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. Such a problem is known to be NP-hard; therefore, efficient algorithms are needed. In particular, computation efficiency is important for mobile robots with limited onboard computation capability. The general problem is known as coverage path planning (CPP). The CPP has several variants for single regions and for disjoint regions. In this research, we are investigating the solutions for disjoint target regions (rooms) that have fixed connection points (doors). In particular, we have found effective simplifications for the cases of rooms with one and two doors, while the challenging case of an unbounded number of rooms can be solved by approximation. As a result, this work presents a divide and conquer strategy (DnCS) to address the problem of finding a path for a sweeping robot that needs to sweep a set of disjoint rooms that are connected by fixed doors and corridors. The strategy divides the problem into computing the sweeping paths for the target rooms and then merges those paths into one solution by optimising the room visiting order. In this strategy, a geometrical approach for room coverage and an undirected rural postman problem optimisation are strategically combined to solve the coverage of the entire area of interest. The strategy has been tested in several synthetic maps and a real scenario showing short computation times and complete coverage.

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“…On the classical approaches, Zhao et al presented a RETRBG algorithm, which is a trajectory replanning method based on guided paths, it can improve the speed and stability of path planning to a certain extent [ 11 ]. Men et al introduced the CPP method based on a Region Optimal Decomposition (ROD), which achieved good results when applied to the path planning of UAV in a port environment [ 12 ]. Mangeruga et al proposed a new diving planning method based on the original underwater pathfinding algorithm, which considered safety constraints and maximizes the number of points of interest visited [ 13 ].…”

Section: Introductionmentioning

confidence: 99%