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An Improved Iterated Greedy Algorithm for Solving Rescue Robot Path Planning Problem with Limited Survival Time
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作者 Xiaoqing Wang Peng Duan +1 位作者 Leilei Meng Kaidong Yang 《Computers, Materials & Continua》 SCIE EI 2024年第7期931-947,共17页
Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning probl... Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm. 展开更多
关键词 Rescue robot path planning life strength improved iterative greedy algorithm problem-specific swap operators
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Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm
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作者 Xiaoge Wei Yuming Zhang +2 位作者 Huaitao Song Hengjie Qin Guanjun Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1295-1316,共22页
Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Fi... Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential. 展开更多
关键词 Sparrow search algorithm optimization and improvement function test set evacuation path planning
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Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm
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作者 Xiaocong Zhou Wei Zheng +2 位作者 Zhaowei Li Panlong Wu Yongjin Sun 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期285-296,共12页
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi... This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results. 展开更多
关键词 Depth Sorting Fast Search algorithm Underwater gravity-aided navigation Path planning efficiency Quick Rapidly-exploring Random Trees*(QRRT*)
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Path Planning for Robotic Arms Based on an Improved RRT Algorithm
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作者 Wei Liu Zhennan Huang +1 位作者 Yingpeng Qu Long Chen 《Open Journal of Applied Sciences》 2024年第5期1214-1236,共23页
The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operationa... The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning. 展开更多
关键词 Robotic Arm Path planning RRT algorithm Adaptive Pruning Optimization
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Research on AGV task path planning based on improved A^(*) algorithm 被引量:1
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作者 Xianwei WANG Jiajia LU +2 位作者 Fuyang KE Xun WANG Wei WANG 《Virtual Reality & Intelligent Hardware》 2023年第3期249-265,共17页
Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in thes... Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles. 展开更多
关键词 Autonomous guided vehicle(AGV) Map modeling Global path planning Improved A^(*)algorithm Path optimization Bezier curves
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Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles
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作者 Kwon Ryong Hong Su Il O +2 位作者 Ryon Hui Kim Tae Song Kim Jang Su Kim 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第4期26-40,共15页
Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the... Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability. 展开更多
关键词 Minimum dose Path planning Nuclear facility inspection ABC algorithm Rao algorithms Swap sequence K-opt operation
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Improved lazy theta algorithm based on octree map for path planning of UAV
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作者 Meng-shun Yuan Tong-le Zhou Mou Chen 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第5期8-18,共11页
This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By us... This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight. 展开更多
关键词 Unmanned aerial vehicle Path planning Lazy theta*algorithm Octree map Line-of-sight algorithm
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Research on Parking Path Planing Based on A-Star Algorithm
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作者 Zhiliang Deng Dong Wang 《Journal of New Media》 2023年第1期55-64,共10页
The issue of finding available parking spaces and mitigating conges-tion during parking is a persistent challenge for numerous car owners in urban areas.In this paper,we propose a novel method based on the A-star algo... The issue of finding available parking spaces and mitigating conges-tion during parking is a persistent challenge for numerous car owners in urban areas.In this paper,we propose a novel method based on the A-star algorithm to calculate the optimal parking path to address this issue.We integrate a road impedance function into the conventional A-star algorithm to compute path duration and adopt a fusion function composed of path length and duration as the weight matrix for the A-star algorithm to achieve optimal path planning.Furthermore,we conduct simulations using parking lot modeling to validate the effectiveness of our approach,which can provide car drivers with a reliable optimal parking navigation route,reduce their parking costs,and enhance their parking experience. 展开更多
关键词 A-Star algorithm path planning intelligent transportation
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LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment
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作者 Xiaoli Li Tongtong Jiao +2 位作者 Jinfeng Ma Dongxing Duan Shengbin Liang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期595-617,共23页
In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone ... In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account. 展开更多
关键词 Unmanned surface vehicles local obstacle avoidance algorithm artificial potential field algorithm path planning collision detection
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A Multi-Strategy-Improved Northern Goshawk Optimization Algorithm for Global Optimization and Engineering Design
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作者 Liang Zeng Mai Hu +2 位作者 Chenning Zhang Quan Yuan Shanshan Wang 《Computers, Materials & Continua》 SCIE EI 2024年第7期1677-1709,共33页
Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the ... Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges. 展开更多
关键词 Northern Goshawk Optimization tent chaotic map T-distribution disturbance state transition algorithm UAV path planning
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Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm
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作者 Shuaixiang Wang 《Journal of Electronic Research and Application》 2023年第1期32-41,共10页
A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the s... A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement. 展开更多
关键词 Parallel ant colony optimization algorithm Dual power sources Distribution network Grid planning
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Distributed collaborative complete coverage path planning based on hybrid strategy
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作者 ZHANG Jia DU Xin +1 位作者 DONG Qichen XIN Bin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第2期463-472,共10页
Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm ... Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably. 展开更多
关键词 multi-agent cooperation unmanned aerial vehicles(UAV) distributed algorithm complete coverage path planning(CCPP)
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Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage
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作者 Junhui Li Yuqing Zhang +4 位作者 Can Chen Xiaoxiao Wang Yinchi Shao Xingxu Zhu Cuiping Li 《Energy Engineering》 EI 2024年第9期2389-2408,共20页
Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the ... Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning. 展开更多
关键词 Zoning control two-stage planning site selection and capacity determination optimized scheduling improved ant lion algorithm
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A path planning method for robot patrol inspection in chemical industrial parks
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作者 王伟峰 YANG Ze +1 位作者 LI Zhao ZHAO Xuanchong 《High Technology Letters》 EI CAS 2024年第2期109-116,共8页
Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to... Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment. 展开更多
关键词 path planning robot patrol inspection iterated local search and random variableneighborhood descent(ILS-RVND)algorithm
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HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT
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作者 李国丽 盛大宁 +3 位作者 王俊椋 景佳 王超 闫冰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2010年第1期97-101,共5页
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an... The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment (IMRT). The objective functions of planning target volume (PTV) and normal tissue (NT) are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications. 展开更多
关键词 gradient methods inverse planning multi-objective optimization hybrid gradient algorithm
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Path planning in uncertain environment by using firefly algorithm 被引量:15
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作者 B.K.Patle Anish Pandey +1 位作者 A.Jagadeesh D.R.Parhi 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2018年第6期691-701,共11页
Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mo... Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches. 展开更多
关键词 Mobile robot NAVIGATION FIREFLY algorithm PATH planning OBSTACLE AVOIDANCE
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Algorithm for Low Altitude Penetration Aircraft Path Planning with Improved Ant Colony Algorithm 被引量:19
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作者 叶文 马登武 范洪达 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2005年第4期304-309,共6页
The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method... The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained. 展开更多
关键词 ant colony algorithm path planning keeping optimization adaptively adiusting low altitude penetration
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Using genetic/simulated annealing algorithm to solve disassembly sequence planning 被引量:5
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作者 Wu Hao Zuo Hongfu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第4期906-912,共7页
Disassembly sequence planning (DSP) plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassem... Disassembly sequence planning (DSP) plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient. 展开更多
关键词 disassembly sequence planning disassembly hybrid graph connection matrix precedence matrix binary-tree algorithms simulated annealing algorithm genetic algorithm.
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Mission Planning and Action Planning for Agile Earth-Observing Satellite with Genetic Algorithm 被引量:5
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作者 Kai Sun Zheng-Yu Yang +1 位作者 Pei Wang Ying-Wu Chen 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第5期51-56,共6页
This paper concerns the mission scheduling problem for an agile Earth-observing satellite. Mission planning and action planning for the satellite are both taking into account. Multiple mission types( including multi-s... This paper concerns the mission scheduling problem for an agile Earth-observing satellite. Mission planning and action planning for the satellite are both taking into account. Multiple mission types( including multi-strip area,real time download request,and stereoscopic request) and complex satellite actions,such as observe action and data download action,are considered in this paper. Through reasonable analysis of specialties and operational constraints of agile satellites in observing process,the mission scheduling model under multiple objective conditions is constructed. A genetic algorithm combined with heuristic rules is designed to solve problem. Genetic algorithm is designed to arrange user missions and heuristic rules are used to arrange satellite actions. Experiment results suggest that our algorithm works well for the agile Earth-observing satellite scheduling problem. 展开更多
关键词 SCHEDULING mission planning action planning agile Earth-observing satellite genetic algorithm
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Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm 被引量:4
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作者 S.Mahmoud Zadeh D.M.W Powers +2 位作者 A.M.Yazdani K.Sammut A.Atyabi 《Journal of Marine Science and Application》 CSCD 2018年第4期585-591,共7页
Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution(DE) algorithm ... Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution(DE) algorithm is employed. The performance of the DE-based planner in generating time-efficient paths to direct the AUV from its initial conditions to the target of interest is investigated within a complexed 3D underwater environment incorporated with turbulent current vector fields, coastal area,islands, and static/dynamic obstacles. The results of simulations indicate the inherent efficiency of the DE-based path planner as it is capable of extracting feasible areas of a real map to determine the allowed spaces for the vehicle deployment while coping undesired current disturbances, exploiting desirable currents, and avoiding collision boundaries in directing the vehicle to its destination. The results are implementable for a realistic scenario and on-board real AUV as the DE planner satisfies all vehicular and environmental constraints while minimizing the travel time/distance, in a computationally efficient manner. 展开更多
关键词 Path planning Differential evolution Autonomous UNDERWATER vehicles EVOLUTIONARY algorithms OBSTACLE AVOIDANCE
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