Earticle

Coverage algorithm is one of the core technologies required for intelligent robots such as cleaning robots, harvesting robots, painting robots and lawn mowing robots. Although many smart coverage algorithms have been proposed, to the best of our knowledge, all of them make the same assumption that they have sufficient time to cover the entire target area. However, the time to completely cover the whole target area may not always be available. Therefore, in this paper, we propose another new coverage scheme, which we call the DmaxCoverage algorithm that decides the coverage path by considering the deadline for coverage. This approach can be beneficial when the time for the coverage is not sufficient to cover the entire target area. Experimental results show that the DmaxCoverage algorithm outperforms previous algorithms for these situations.

Optimal coverage of wireless sensor networks is one of the most fundamental problems for constructing efficient perception layer of the Internet of Things. On the basis of research on spatial neighborhood, the node coverage and area coverage models are analyzed, then an optimal coverage algorithm of wireless sensor networks is proposed based on particle swarm optimization with coherent velocity. Experimental results show that the algorithm can significantly improve the network coverage; in addition, the coherent velocity can effectively avoid network prematurely into a local optimal solution, so as to enhance the network coverage.

Maximize coverage and prolong the network lifetime for wireless sensor networks has become one of the key topics of research. For this, put forward a kind of node scheduling strategy based on covering algorithm. The algorithm by Poisson distribution model structure node density formula, according to the node density formula in the monitoring region deploying to effective coverage; On the other hand, through the node state scheduling mechanism updates as well as to the neighbor node, can be made of residual energy of nodes and monitoring the area sensor the node energy consumption balance, so as to prolong the network lifetime goal. The simulation results show that, the algorithm cans not only useless nodes completely too effective coverage, improve the coverage while optimizing the cyber source configuration, prolong the network lifetime.

The energy aware based active node decision and the sensing directions thereof become the research hotspots of DNN (Directional Sensor Network). Therefore, an EACO (Energy aware-based coverage optimal) algorithm is proposed in this paper. Specifically, each cluster head decides the number of the active nodes of the cluster members and the sensing directions thereof according to the coverage area of the sensor nodes and the energy information thereof. Meanwhile, the dormancy mechanism is adopted to minimize the overlapping area among the clusters. The simulation result shows that the proposed EACO algorithm is improved in the aspects of coverage rate and network lifetime.

Target area coverage is the most fundamental goal of WSN (Wireless Sensor Network) application, so the design of energy aware based algorithm for coverage time maximization becomes a core problem of a large network. For this reason, a DECAR (Distributed-energy and Coverage Aware Routing) protocol is proposed in this paper to realize the goal of network coverage maximization. Specifically, the rest energy and the coverage overlapping degree of the nodes are considered during CH (cluster head) selection process so as to provide more opportunities for the nodes with more rest energy or higher coverage overlapping degree to become CH; like this, the nodes with less rest energy will not be selected as CH, thus to avoid shortening the network lifetime due to early failure of these nodes and balance the network energy consumption. Meanwhile, the main data transmission line composed of CHs is constructed in the data transmission stage in order to improve data transmission efficiency. The simulation result shows: compared with CPCP-ea and EEUC protocols, the proposed DECAR protocol can obtain longer network lifetime and better data coverage ratio.

Coverage is a key and fundamental problem in wireless sensor networks. To provide high quality of service, high coverage ratio of sensor nodes in monitored area should be assured. Coverage holes may lead to routing failure and degrade the quality of service. A novel coverage hole discovery algorithm, VCHDA, is proposed for wireless sensor networks in this paper. The proposed algorithm is based on the well-known Voronoi diagram. It can recognize coverage holes and label the border nodes of coverage holes effectively. Simulations are conducted and the results show that the proposed algorithm is effective and with high accuracy.

The process of verifying the modified software in the maintenance phase is called Regression Testing. The size of the regression test suite and its selection process is a complex task for regression testers because of time and budget constraints. In this research paper, the Bee Colony Optimization (BCO) algorithm for the fault coverage regression test suite prioritization has been presented. In the natural bee colony, there are of two types of worker bees; Scout bees and forager bee, who are responsible for the development and maintenance of the colony. The BCO algorithm developed for the fault coverage regression test suite is based on the behavior of these two bees. The BCO algorithm has been formulated for fault coverage to attain maximum fault coverage in minimal units of execution time of each test case, using two examples whose results are comparable to optimal solution. Average Percentage of Fault Detection (APFD) metrics and charts has been used to show the effectiveness of proposed algorithm.

One major problem in the area of wireless sensor networks is the coverage problem. The coverage problem deals with the ability of the network to cover a certain area or some certain events. In this paper, we focus on the area coverage problem. We use a cluster-based coverage control scheme and propose HSSAC protocol to maintain sensing coverage by keeping a small number of active sensor nodes and a small amount of energy consumption in a wireless sensor network. In this protocol, proper active sensor set can be determined using the harmony search algorithm. Due to the proposed protocol accuracy in selecting the active sensor set, it is able to provide the acceptable coverage rate in sparse deployment. As the result of increasing nodes density, the proposed protocol decreases the number of active nodes in the sensor networks. Thus, the proposed protocol decreases the energy consumption of the networks and prolongs the network lifetime. We have simulated our protocol and simulation results show high efficiency of the proposed protocol.

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that artificial fish swarm algorithm may easily lead to regional optimization. As for this, the global searching ability of algorithm is improved and the solving efficiency is optimized. Moreover, the algorithm is able to adapt to complicated environment. Shown by related simulation experiment, the improved fish swarm algorithm could effectively optimize sensor network node deployment and improve network coverage rate. Compared with basic artificial fish swarm algorithm, improved fish swarm algorithm increases network coverage rate by 8.9%.

This paper presents a kind of coverage optimization strategy based on improved artificial fish swarm algorithm for wireless sensor networks, by adaptively adjusting the vision range and the step length of artificial fish swarm the accuracy of optimization, convergence speed and stability are improved, then combining with the performance of WSN network coverage, the network coverage can be optimized. The simulation results show that comparing to the basic artificial fish swarm algorithm, the network coverage ratio of improved artificial fish swarm algorithm improves 17%.

According to the maximum coverage problem in wireless sensor networks, GA algorithm combined with the standard processing method can improve the network coverage based on, but there is a risk of falling into local optimum, and costs more computation time. On the condition of analysis and proof of the effectiveness by the employment of normalization processing to resolve coding redundancy of MCSDP, further details of the evaluate conditions are proposed, and new parent selection mechanism is introduced, which both are verified by compared experiments. The experiment results show that the optimization processing method proposed in this paper retains the characteristics of existing methods, and has better optimization performance and improves the network coverage rate as well as calculation speed, which verifies the effectiveness and superiority of the method proposed in this paper.

Purpose: This study was conducted to develop an optimal combine scheduling program using Max-Coverage algorithm which derives the maximum efficiency for a specific location in harvest seasons. Methods: The combine scheduling program was operated with information about combine specification and farmland. Four operating types (Max-Coverage algorithm type, Boustrophedon path type, max quality value type, and max area type) were selected to compare quality and working capacity. Result: The working time of Max-Coverage algorithm type was shorter than others, and the total quality value of Max-Coverage algorithm and max quality value type were higher than others. Conclusion: The developed combine scheduling program using Max-Coverage algorithm will provide optimal operation and maximum quality in a limited area and time.

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

In deep-sea mining, effective path planning for vehicle clusters is crucial. Notably, few existing path-planning studies consider pipeline-related factors. This paper presents a master-sub algorithm fusion strategy for the full-coverage path planning of deep-sea mining vehicle clusters in a pipe-lifting system. The master-algorithm uses an improved DDQN algorithm to cover mining areas efficiently and avoid hose entanglement. The sub-algorithm combines a modified inner-spiral algorithm with an A ^* algorithm for wide-area coverage. Experiments show this strategy achieves 100% coverage in complex environments, prevents hose entanglement, and enables vehicle and hose recovery.

This Paper is to find out a solution for the full-coverage algorithm requiring the real-time processing such as mobile home service robots and vacuum cleaner robots. Previous methods are used by adopting based grid approach method. They used lots of sensors, a high speed CPU, expensive ranger sensors and huge memory. Besides, most full-coverage algorithms should have a map before obstacle avoidance. However, if a robot able to recognize the tangent vector of obstacles, it is able to bring the same result with less sensors and simplified hardware. Therefore, this study suggests a topological based approach and a local obstacle voidance method using a few of PSD sensors and ultra sonic sensors. The simulation results are presented to prove its applicability.

최근 슬램 알고리즘의 실현을 통해 주변 환경에 대한 맵 정보가 획득 가능할 경우에 격자 그리드 기반의 Boustrophedon 경로 기반 커버리지 알고리즘이 매우 효율적인 것으로 알려져 있다. 그러나 Boustrophedon 경로 기반 알고리즘은실내 공간에 장애물이 복잡하게 존재할 경우에는 급격히 성능 저하현상이 발생한다. 따라서 본 논문에서는 복잡한 실내 공간에서도 효율적으로 빠른 시간 내에 청소를 완료할 수 있는 Group-k 알고리즘을 제안하고 구현한다. Group-k 알고리즘은 전체 공간을 장애물의 복잡성에 근거하여 전체 공간을 그룹화하고 각 그룹별 우선순위를 부여하여 전체 작업 순서를 효율적으로 제어한다. 구현 기반의 실험에 의하면, 본 논문에서 제안된 알고리즘은 Boustrophedon 경로 기반 알고리즘에 비해 약 20%의 성능 향상을 보여준다.

The coverage algorithm based on Boustrophedon path has been known to be the most efficient in places without or less obstacles If the map of an environmental area thru SLAM algorithm can be obtainable. However, the efficiency of the coverage algorithm based on Boustrophedon path drops drastically when obstacles are complex. In this paper, we propose and implement a new algorithm, which we call Group-k, that efficiently handles the complex area. The Group-k algorithm groups the obstacles and prioritizes the covering sequences with complex rank of the groups. Implementation-based experiments show about 20% improved performance when applying the nor- algorithm, compared to the Boustrophedon path algorithm.

본 논문에서는 청소 로봇이 미지의 큰 맵에 대해 맵-분해를 적용할 수 있는 확장된 BSA(backtracking spiral algorithm) 커버리지 경로계획(coverage path planning)을 제안한다. 기존의 나선형 경로에 기반한 방법은 전체 맵을 로봇의 크기만큼 나누고 각 격자에서 맵 정보를 기록한 격자들을 통해서 청소 맵에 대한 커버리지 경로계획을 구현할 수 있지만 격자 수량이 많을수록 알고리즘의 성능이 떨어지는 단점이 존재한다. 제안한 방법은 큰 맵에서 맵 축소 및 $A^*$ 탐색 기법을 이용하여 맵을 분해하고 맵의 크기 조절을 통해서 격자의 수량을 효율적으로 제어할 수 있기 때문에 기존의 방법의 단점을 극복할 수 있을 뿐만 아니라 분해된 작은 맵에서 기존의 BSA기법을 사용하여 나선형 경로에 기반한 커버리지 경로계획의 장점도 유지할 수 있다. 일련의 실험을 통하여 제안된 방법은 미지의 큰 맵에서 커버리지 경로계획을 보다 효과적으로 적용이 가능하였고, 기존의 BSA 기법의 성능을 효율적으로 개선할 수 있었다.

In this paper we propose an extended BSA (Backtracking Spiral Algorithm) which uses coverage path planning method for mapdecomposition of large unknown maps. The existing spiral path planning method divides the entire map into grids by the size of the cleaning robot and stores the grid information which is then used to generate the cleaning path. The main disadvantage of this existing method is that when the size of map increases the grid information necessary to complete the cleaning path generation also increases which is less efficient. The proposed method thus overcomes the disadvantages of existing method by effectively controlling the size of map to form a contracted map and by using $A^*$ method for map-decomposition. As the proposed method can effectively control the number of grid it not only overcomes the disadvantages of the existing spiral path planning method but also maintains the advantages of using BSA in the decomposed map. Through a series of experiments it was found that the proposed method can effectively apply the coverage path planning in large unknown maps and improve the performance of the existing BSA method.

이 논문은 지능형 로봇을 위한 새로운 커버리지 알고리즘을 제안한다. 커버리지 알고리즘의 성능을 향상하기 위한 많은 연구들은 전체 커버리지 완료 시간을 최소화하는데 초점을 맞추어왔다. 그러나, 만일 전체 커버리지를 완료하기에 충분한 시간이 없다면, 최적의 경로는 달라질 수 있다. 이러한 문제를 해결하기 위하여 본 논문에서는 MaxCoverage라고 하는 데드라인이 있을 경우에 가능한 많은 면적을 커버하기 위한 새로운 커버리지 알고리즘을 제안한다. MaxCoverage 알고리즘은 이동 경로를 셋 커버 문제를 위한 그리디 알고리즘을 이용하여 결정한다. 실험 결과에 의하면 MaxCoverage 알고리즘은 임의의 데드라인에 대하여 다른 알고리즘들에 비해 향상된 성능을 보여준다.

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.