TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving i...TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.展开更多
In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flock...In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.展开更多
The interest shown by some community of researchers to autonomous drones or UAVs （unmanned aerial vehicles） has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate mo...The interest shown by some community of researchers to autonomous drones or UAVs （unmanned aerial vehicles） has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we proposed a flocking-based routing protocol for UAVs called BR-AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. Moreover, an automatic ground base stations discovery mechanism has been introduced for a proactive drones and ground networks association needed for the context of real-time applications. The performance of BR-AODV was evaluated and compared with that of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.展开更多
文摘TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.
文摘In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.
文摘The interest shown by some community of researchers to autonomous drones or UAVs （unmanned aerial vehicles） has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we proposed a flocking-based routing protocol for UAVs called BR-AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. Moreover, an automatic ground base stations discovery mechanism has been introduced for a proactive drones and ground networks association needed for the context of real-time applications. The performance of BR-AODV was evaluated and compared with that of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.
