增强动态环境中用于拍摄群体行为的多无人机协同
Enhancing Multi-Drone Coordination for Filming Group Behaviours in Dynamic Environments
AI总结：
本文针对动态环境中多角度同时拍摄群体行为的问题，提出基于编队的拍摄指令与基于冲突的MAPF算法，实现高效在线任务分配与无碰撞路径规划，适用于多种拍摄和覆盖场景。
摘要：
多智能体路径规划（MAPF）是机器人与人工智能领域的基础问题，在现实场景中具有众多应用。其中一种场景是拍摄包含多个演员的场景，目标是从多个角度同时捕捉画面。在此，我们提出一种基于编队的拍摄指令，先进行任务分配，随后采用基于冲突的MAPF算法，为多个智能体规划高效路径，以在避免碰撞的同时实现拍摄目标。我们提出对标准MAPF公式的扩展，以适应特定演员的要求和约束。该方法结合了Conflict-Based Search，这是一种广泛用于求解MAPF问题的启发式搜索技术。我们通过在模拟环境中的多种MAPF场景实验，验证了该方法的有效性。所提算法实现了基于编队拍摄的高效在线任务分配，能够捕捉动态场景，使其适用于各种拍摄和覆盖应用。
英文摘要：
Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and AI, with numerous applications in real-world scenarios. One such scenario is filming scenes with multiple actors, where the goal is to capture the scene from multiple angles simultaneously. Here, we present a formation-based filming directive of task assignment followed by a Conflict-Based MAPF algorithm for efficient path planning of multiple agents to achieve filming objectives while avoiding collisions. We propose an extension to the standard MAPF formulation to accommodate actor-specific requirements and constraints. Our approach incorporates Conflict-Based Search, a widely used heuristic search technique for solving MAPF problems. We demonstrate the effectiveness of our approach through experiments on various MAPF scenarios in a simulated environment. The proposed algorithm enables the efficient online task assignment of formation-based filming to capture dynamic scenes, making it suitable for various filming and coverage applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
