The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightwei...The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance.The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)module.Additionally,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection head.Based on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments.In particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability.To verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was created.The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.展开更多
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
This paper deeply explores the autonomous collision avoidance algorithm for intelligent ships,aiming to enhance the intelligence level and safety of ship collision avoidance by integrating navigation experience.An aut...This paper deeply explores the autonomous collision avoidance algorithm for intelligent ships,aiming to enhance the intelligence level and safety of ship collision avoidance by integrating navigation experience.An autonomous collision avoidance algorithm based on navigation experience is designed,a collision avoidance experience database is constructed,a quantitative model is established,and specific algorithm steps are implemented.The algorithm is verified and analyzed through simulation tests.The results show that the algorithm can effectively achieve autonomous ship collision avoidance in different scenarios,providing new ideas and methods for the development of intelligent ship collision avoidance technology.展开更多
Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,...Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,it has become a traffic congestion and accident-prone area,which seriously affects road traffic safety and vehicle traffic efficiency.Therefore,it is of great significance to study the collaborative control strategy of urban intersections.This paper analyzes and summarizes the methods of intersection cooperative control based on intelligent connected vehicles in recent years,and looks forward to the future development trend and prospects of the combination of intersection cooperative control and Vehicle-Infrastructure Cooperation System.展开更多
基金supported by Xinjiang Uygur Autonomous Region Metrology and Testing Institute Project(Grant No.XJRIMT2022-5)Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD0012).
文摘The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance.The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)module.Additionally,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection head.Based on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments.In particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability.To verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was created.The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.
基金This work was supported by the Postdoctoral Fund of FDCT,Macao(Grant No.0003/2021/APD).Any opinions,findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
文摘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.
基金Research and Development of Unmanned Vessel System Based on Intelligent Ship-Shore Collaborative Technology,Hainan University of Science and Technology Science Research(HKKY2024-79)。
文摘This paper deeply explores the autonomous collision avoidance algorithm for intelligent ships,aiming to enhance the intelligence level and safety of ship collision avoidance by integrating navigation experience.An autonomous collision avoidance algorithm based on navigation experience is designed,a collision avoidance experience database is constructed,a quantitative model is established,and specific algorithm steps are implemented.The algorithm is verified and analyzed through simulation tests.The results show that the algorithm can effectively achieve autonomous ship collision avoidance in different scenarios,providing new ideas and methods for the development of intelligent ship collision avoidance technology.
基金Tinajin Research Tnnovation Project for Postgraduate Students:Research on multi-sensor fusion vehicle detection algorithm in complex weather conditions(2020YJSS086).
文摘Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,it has become a traffic congestion and accident-prone area,which seriously affects road traffic safety and vehicle traffic efficiency.Therefore,it is of great significance to study the collaborative control strategy of urban intersections.This paper analyzes and summarizes the methods of intersection cooperative control based on intelligent connected vehicles in recent years,and looks forward to the future development trend and prospects of the combination of intersection cooperative control and Vehicle-Infrastructure Cooperation System.
