Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network m...Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.展开更多
Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumul...Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper.First,the traditional random initialization is replaced by sine chaotic mapping,making the initial population more evenly distributed in the search space and improving the quality of the initial solution.Since the global search ability of the crested porcupine optimizer(CPO)is limited,the Cauchy inverse cumulative distribution strategy is introduced.In addition,as CPO is prone to fall into local optima in later stages,a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation,thereby improving the algorithm’s ability to escape from local optima.Finally,ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets.The experimental results show that ICPO has excellent competitiveness and optimization performance.The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms.The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity,demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.展开更多
In recent years,the crop protection unmanned aerial vehicle(UAV)has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport.Howev...In recent years,the crop protection unmanned aerial vehicle(UAV)has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport.However,there are few researches on obstacle-avoiding path planning for crop protection UAV.In this study,an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints.First,according to the flight parameters of UAV and the types of obstacles in the field,the obstacle circle model and the small obstacle model were established.Second,after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles,the genetic algorithm(GA)was used to search the optimal obstacle-avoiding path.Third,for turning in the path planning,a strategy considering the size of the spray width and the UAV’s minimum turning radius was presented,which could decrease the speed change times.The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%,while the path length increased by only 1.6%in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions.With the increase of obstacle radius,the area of overlap and skip reduced effectively with no significant increase in path length.Therefore,the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight.展开更多
In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this st...In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this study,for planning rapidly changing UAV swarms,we propose a dynamic value iteration network(DVIN)model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function,which enables UAVs/nodes to adapt to novel physical locations.We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method.Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60105003) and the Natural Science Foundation of Zhejiang Province (No. 600025), China
文摘Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.
基金supported by the National Natural Sci-ence Foundation of China(No.62102373,No.62273243,and No.62473341)Henan Province Key R&D Project(No.241111210400)Joint Fund Key Project of science and Technology R&D Plan of Henan Province(No.235200810022).
文摘Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper.First,the traditional random initialization is replaced by sine chaotic mapping,making the initial population more evenly distributed in the search space and improving the quality of the initial solution.Since the global search ability of the crested porcupine optimizer(CPO)is limited,the Cauchy inverse cumulative distribution strategy is introduced.In addition,as CPO is prone to fall into local optima in later stages,a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation,thereby improving the algorithm’s ability to escape from local optima.Finally,ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets.The experimental results show that ICPO has excellent competitiveness and optimization performance.The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms.The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity,demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.
基金This research was supported by Natural Science Foundation of Heilongjiang Province of China(No.C2018023)China Postdoctoral Science Foundation(No.2015M580254,No.2017T100221)+1 种基金Heilongjiang Postdoctoral Science Foundation(No.LBH-Z15011)The authors would like to thank the anonymous reviewers for their helpful suggestions,which greatly improved the paper.
文摘In recent years,the crop protection unmanned aerial vehicle(UAV)has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport.However,there are few researches on obstacle-avoiding path planning for crop protection UAV.In this study,an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints.First,according to the flight parameters of UAV and the types of obstacles in the field,the obstacle circle model and the small obstacle model were established.Second,after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles,the genetic algorithm(GA)was used to search the optimal obstacle-avoiding path.Third,for turning in the path planning,a strategy considering the size of the spray width and the UAV’s minimum turning radius was presented,which could decrease the speed change times.The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%,while the path length increased by only 1.6%in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions.With the increase of obstacle radius,the area of overlap and skip reduced effectively with no significant increase in path length.Therefore,the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight.
基金Project supported by the National Natural Science Foundation of China(No.61501399)the SAIC MOTOR(No.1925)the National Key R&D Program of China(No.2018AAA0102302)。
文摘In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this study,for planning rapidly changing UAV swarms,we propose a dynamic value iteration network(DVIN)model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function,which enables UAVs/nodes to adapt to novel physical locations.We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method.Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.
